NASA/TM-2002-

SIMBIOS Project 2001 Annual Report

Giulietta S. Fargion, Science Applications International Corporation, Maryland

Charles R. McClain, Goddard Space Flight Center, Greenbelt, Maryland

National Aeronautics and

Space Administration

Goddard Space Flight Center

Greenbelt, Maryland 20771

February 2002
SIMBIOS Project Annual Report

ii

Preface

The purpose of this technical report is to provide current documentation of the the Sensor Intercomparison and Merger

for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) Project activities, NASA Research Announcement

(NRA) research status, satellite data processing, data product validation, and field calibration. This documentation is

necessary to ensure that critical information is related to the scientific community and NASA management. This critical

information includes the technical difficulties and challenges of validating and combining ocean color data from an

array of independent satellite systems to form consistent and accurate global bio-optical time series products. This

technical report is not meant as a substitute for scientific literature. Instead, it will provide a ready and responsive

vehicle for the multitude of technical reports issued by an operational project.

The SIMBIOS Science Team Principal Investigators (PIs) original contributions to this report are in chapters four and

above. The purpose of these contributions is to describe the current research status of the SIMBIOS-NRA-96 funded

research. The contributions are published as submitted, with the exception of minor edits to correct obvious

grammatical or clerical errors.
SIMBIOS Project Annual Report

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Table of Contents

1. AN OVERVIEW OF SIMBIOS PROJECT ACTIVITIES AND ACCOMPLISHMENTS DURING FY01 .............................................. 1

2. SIMBIOS: SCIENCE TEAM AND CONTRACTS ........................................................................................................................ 5

3. SIMBIOS PROJECT DATA PROCESSING AND ANALYSIS RESULTS ........................................................................................ 8

4. ADAPTATION OF A HYPERSPECTRAL ATMOSPHERIC CORRECTION ALGORITHM FOR MULTI-SPECTRAL OCEAN

COLOR DATA IN COASTAL WATERS........................................................................................................................................ 28

5. BIO-OPTICAL MEASUREMENTS IN UPWELLING ECOSYSTEMS IN SUPPORT OF SIMBIOS .................................................... 31

6. SATELLITE OCEAN-COLOR VALIDATION USING SHIPS OF OPPORTUNITY............................................................................ 40

7. MERGING OCEAN COLOR DATA FROM MULTIPLE MISSIONS .............................................................................................. 50

8. BIO-OPTICAL AND REMOTE SENSING OBSERVATIONS IN CHESAPEAKE BAY ...................................................................... 52

9. REFINEMENT OF PROTOCOLS FOR MEASURING THE APPARENT OPTICAL PROPERTIES OF SEAWATER................................. 63

10. OPTIMIZATION OF OCEAN COLOR ALGORITHMS: APPLICATION TO SATELLITE AND IN SITU DATA MERGING. ............... 73

11. AEROSOL OPTICAL PROPERTIES OVER THE OCEANS: A SUMMARY OF THE FRSR DATABASE, CALIBRATION

PROTOCOL AND MEASUREMENT UNCERTAINTIES ................................................................................................................... 77

12. BIO-OPTICAL MEASUREMENT AND MODELING OF THE CALIFORNIA CURRENT AND SOUTHERN OCEANS......................... 84

13. VARIABILITY IN OCEAN COLOR ASSOCIATED WITH PHYTOPLANKTON AND TERRIGENOUS MATTER: TIME SERIES

MEASUREMENTS AND ALGORITHM DEVELOPMENT AT THE FRONT SITE ON THE NEW ENGLAND CONTINENTAL SHELF...... 92

14. OCEAN OPTICS PROTOCOLS AND SIMBIOS PROTOCOL INTERCOMPARISON ROUND ROBIN EXPERIMENTS (SPIRREX).. 99

15. BERMUDA BIO OPTICS PROJECT...................................................................................................................................... 101

16. PLUMES AND BLOOMS: MODELING THE CASE II WATERS OF THE SANTA BARBARA CHANNEL...................................... 108

17. ALGORITHMS FOR PROCESSING AND ANALYSIS OF OCEAN COLOR SATELLITE DATA FOR COASTAL CASE 2 WATERS... 112

18. FACTORS AFFECTING, AND IMPACT OF, DIAZOTROPHIC MICROORGANISMS IN THE WESTERN EQUATORIAL ATLANTIC. 121

19. HPLC PIGMENT MEASUREMENTS FOR ALGORITHM DEVELOPMENT AND VALIDATION IN SUPPORT OF THE

SIMBIOS SCIENCE TEAM..................................................................................................................................................... 125

20. ASSESSMENT, VALIDATION, AND REFINEMENT OF THE ATMOSPHERIC CORRECTION ALGORITHM FOR THE OCEAN

COLOR SENSORS.................................................................................................................................................................... 130

21. MEASUREMENTS OF THE VERTICAL STRUCTURE OF AEROSOLS AND CLOUDS OVER THE OCEAN USING

MICRO-PULSE LIDAR SYSTEMS ........................................................................................................................................... 136

22. MEASUREMENTS AND MODELING OF APPARENT OPTICAL PROPERTIES OF OCEAN WATERS IN SUPPORT TO OCEAN

COLOR DATA CALIBRATION, VALIDATION, AND MERGING ..................................................................................................... 140
SIMBIOS Project Annual Report

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23. CONTRIBUTION TO A GLOBAL CLIMATOLOGY OF SURFACE PIGMENTS FROM OCEAN COLOR SENSORS........................ 147

24. CNES CONTRIBUTION TO OCEAN COLOR CALIBRATION : MULTI-SENSOR CROSS-CALIBRATION OVER DESERT SITES .. 159

25. SUMMARY OF THE OSMI RADIOMETRIC CALIBRATION EFFORTS ................................................................................... 164

26. EVALUATION AND IMPROVEMENT OF THE ATMOSPHERIC CORRECTION AND BIO-OPTICAL ALGORITHMS FOR

THE BLACK AND BARENTS SEAS ........................................................................................................................................... 170

27. VALIDATION OF SEAWIFS OCEAN AND ATMOSPHERIC PRODUCTS IN THE MEDITERRANEAN SEA ............................... 176
SIMBIOS Project Annual Report

1

Chapter 1

An Overview of SIMBIOS Project Activities and

Accomplishments During FY01

Charles R. McClain

NASA Goddard Space Flight Center, Greenbelt, Maryland

Giulietta S. Fargion

 Science Applications International Corporation (SAIC), Beltsville, Maryland

In FY01, the SIMBIOS Project brought a number of

activities to fruition and laid the groundwork for FY02

initiatives. With SeaWiFS continuing to perform well, the

Terra platform and MODIS launched in December 1999, and

several other global missions scheduled for launch (e.g., the

Aqua platform with MODIS in 2002, the ENVISAT mission

with MERIS in 2002, and ADEOS-II with GLI and POLDER

in 2002), the first real opportunties to merge global data sets

can be pursued. NASA HQ approved a three-year

continuation of the science team (2001-2003) under an NRA

and the Project Office. As it is taking some time for the

MODIS team to work through the initial on-orbit processing

issues, data merger-related activities within the Project Office

have been in the development of data merging algorithms and

software to read MODIS, development of capabilities for

working with multiple global data sets in the future (i.e.,

OCTS-GAC reprocessing) and supporting the characterization

of OSMI. These activities and accomplishments are described

below.

SIMBIOS Science Team Support

a. The fifth science team meeting was held in Baltimore in

January 2002 with a substantial US (SIMBIOS and

MODIS PIs) and international participation, with official

representation from OCTS, POLDER-I and II, MOS,

MERIS, GLI, and VIIRS misssions. During the meeting

the team addressed atmospheric correction and biooptical algorithms, a MODIS data merging plan for

2002, uncertainty budget and sources from in situ

observation, SeaWIFS re-processing, protocols updates

and international collaborations. The agenda,

presentations, minutes and recommendations are posted

at http://simbios.gsfc.nasa.gov/Info/

b. Under a SIMBIOS NRA released in late 1999, a new

science team was selected and has been funded under

contracts or interagency agreements (2000-2003). The

initial team consists of 19 U.S. and 12 international

investigations with additional international

investigations to be added as post-NRA proposals are

received and accepted (i.e., Australian and Korean

proposals). Agreements with the international team

members were initiated by NASA/HQ in Fall 2000 and

most of them have now been signed.

c. In October 2001 the SIMBIOS Project management

conducted team performance evaluations on all

investigations. Formal evaluations of investigations

funded through contracts are required by the NASA

Procurement Office. All investigations, with the

exclusion of one, where renewed for the second year

(2002).

Satellite Data Characterization

a. KOMPSAT/OSMI characterization work was done in

collaboration with KARI. Joint scientific papers were

presented at the Fall AGU meeting in San Francisco

(Franz and Kim, 2002; Kim et al. 2002). In addition,

KARI requested assistance from the Project to identify a

schedule to record onboard OSMI data. Our

recommendation is now implemented.

b. The Project is hosting a NASDA representative (Mr.

Tanaka) for one year (June 2001-June 2002) at NASA

Goddard Space Flight Center to assist in the GLI

preparations. The Project worked on a document entitled

”Instrument characterization of the GLI”, delivered at

the GLI science team meeting in Tokyo (November

2001).

Satellite Data Analyses

a. OCTS GAC data reprocessing was completed. This was

a very productive collaboration effort with NASDA and

Japanese scientists. A scientific presentation was given

at the Fall 2001 AGU meeting in San Francisco (Tanaka

et al. 2001). OCTS-GAC data is available through the
SIMBIOS Project Annual Report

2

GSFC DAAC, the SIMBIOS Project and NASDA.

Descriptions of the data processing stream, OCTSspecific modification to the algorithms, and statistical

comparison between OCTS & SeaWiFS can be found at:

http://seawifs.gsfc.nasa.gov/SEAWIFS/RECAL/OCTS_

Repro1/

b. In collaboration with KARI, SIMBIOS Project worked

on: 1) evaluating the of TRW pre-launch calibration of

OSMI; 2) adapting SIMBIOS software (MSL12) to

process OSMI data and retrieve oceanic optical

properties; 3) developing OSMI vicarious calibration

using in situ (MOBY) and/or another sensor; 4)

evaluating the quality of derived ocean color products.

Scientific presentations were given at the Fall 2001 AGU

meeting in San Francisco (Kim et al., 2001; Franz and

Kim, 2001).

c. Reception, processing, distribution of IRS-P3 MOS data

continued operationally. The data is a realtime broadcast

as captured at Wallops Flight Facility using a subsystem

purchased from a firm in India. Therefore, the coverage

is the western North Atlantic and eastern continental

U.S. and Canada. When the spacecraft is configured for

MOS data collection (periods of space data collection

using another instrument precludes MOS operations), the

data is captured, networked to the Project Office,

processed, and distributed on-line via a browse

capability. Presentations on long-term intercomparison

on MOS and SeaWiFS were given at the MOS workshop

in Berlin and at the Fall AGU in San Francisco (Franz

and Gales, 2001; Gales and Franz 2001).

d. The calibrations of the OCTS and POLDER using

MOBY comparisons and the SeaWiFS atmospheric

correction scheme were completed and published (Wang

et al. In press).

Sunphotometer Data Acquisition

a. A total of 14 coastal and island SIMBIOS CIMEL stations

(1998-2001) were contributed to the NASA AERONET

network. This year marks the end of Project supported

AERONET coastal and island CIMEL sites. Starting from

January 2002, the SIMBIOS CIMELs will be operated

and be maintained by AERONET.

b. This year, SIMBIOS CIMEL sun photometer data were

used by the community in more than 20 scientific papers.

c. PREDE characterization and instrument hardening are

ongoing. PREDE and MPL were deployed during ACEASIA international campaign.

d. All sun photometer calibrations are done at GSFC. A

consistent end-to-end data processing procedure was

developed for the suite of sun photometers that provide

data to the Project. The different instruments include the

Microtops hand-held instrument, CIMEL, the Brookhaven

shadow-band radiometer, SIMBAD, and PREDE. The

analysis procedures include calibration, derived product

generation, quality control, archival in SeaBASS, and

satellite match-up analyses. The procedures are

documented in a NASA technical memorandum

published in early 2001 (Fargion, Barnes and McClain

2001) and scientific papers presented at the Fall 201 AGU

meeting in San Francisco (Pietras et al. 2001;

Knolelspiesse et al. 2001).

e. Global Marine Atmosphere Database: The Project began

work with Dr. Frouin (SIO), Dr. Miller (BNL) and

AERONET to create an AOT database in order to

develop alternative atmospheric models from those of

Shettle and Fenn.

Bio-Optical Data Evaluation And Synthesis

a. Multisensor in-situ bio-optical and atmospheric match-up

analysis routines are in place for MOS, SeaWiFS, OCTSGAC, OSMI and MODIS (Bailey et al. 2001).

b. The SeaBASS data volume continues to grow steadily as

Science Team members and others submit new data sets.

A new SeaBASS data archive and relational database

were developed (Werdell et al. 2001). We now have

password-protected and public SeaBASS archive

versions. Public data is currently from 1975 to 1999 and

includes all data collected by the first SIMBIOS Team.

An agreement is in place between the National

Oceanographic Data Center (NODC) and the Project for

the distribution of the SeaBASS CD-ROM and data

archive depository. All SeaBASS data collected prior to

31 December 1999 has been submitted to NODC.

c. The sun photometer data from the coastal CIMEL sites

and the shipboard observations are also being ingested

into the archive. Data quality control tests are being

refined and automated in order to keep up with the

increasing volume of new data. In order to make

SeaBASS more efficient and easier to use, the archive

was redesigned and rebuilt over the past year.

d. The in situ measurement protocols were updated,

expanded (version 3) and are in press (Mueller and

Fargion, 2002). The format of this protocol manual was

changed so that specific topics were included as chapters

with different groups of authors for each chapter.

Data Merging

a. Algorithms for combined extraction and processing of

MODIS and SeaWiFS bins have been developed. The

Project has collaborated with University of Miami (Dr.

Evans) on the comparison of MODIS and SeaWiFS ocean

color retrievals for overlapping bins with water-leaving

radiance and chlorophyll products.

b. A Neural Network (NN) merging technique was applied

to SeaWiFS-MODIS (level 3), and a wavelet merging

technique was applied to SeaWiFS-MOS (level 2). A

scientific presentation was given at the Fall 2001 AGU

meeting in San Francisco (Kwiatkowska-Ainsworth,

2002).
SIMBIOS Project Annual Report

3

c. A diagnostic data set was implemented at different ocean

sites and is intended to facilitate future data merging

activities. The regions were discussed at several

SIMBIOS Team and IOCCG meetings. Approximately 30

SeaWiFS ocean sites were implemented for this purpose

as a collaboration of SeaWiFS and SIMBIOS Projects.

We had constant and large browse and data download

traffic of these ocean sites. MOS and OCTS-GAC

diagnostic data sets will be made available in winter 2002.

Radiometric Data Quality Assurance

a. SIMBIOS Radiometric Intercomparison (SIMRIC-1) was

performed as a round robin in which radiance was

measured at 5 laboratories and at 3 companies (Satlantic

Inc., Biospherical Instruments Inc., HOBI Labs) with the

NIST-calibrated SXR-II. Agreement was always (with

one exception) within combined uncertainties (about +/-

2%). Spectralon bi-directional reflectance factors are an

important source of uncertainty. A scientific presentation

was given at the Fall 2001 AGU meeting in San Francisco

(Meister et al., 2002).

b. SeaWiFS transfer Radiometer (SXR-II) was calibrated

with SIRCUS at NIST in December 2000 and December

2001.

c. The SXR-IIx was monitored from November 2000 to

December 2001 by a portable light source, the SQM-II (~

50 HiBank and ~ 50 LoBank mode measurements).

d. SXR-II measurements were made of the following

sources: NIST NPR, GSFC Hardy and Code 916 sphere

(6 times or more).

e. The Project participated in EOS and NIST radiometric

intercomparison round robin at GSFC (April 2001).

Support Services

a. The Project Office provided cruise support (satellite

coverage information, near-realtime data products,

instrument pool, etc.) to 282 cruises to date (not all by

Science Team members) including ACE-Asia (2-month)

and a SIMBIOS Validation Study (6-month) in the

Atlantic Ocean and Antarctic on the R/V Akademik Ioffe.

This campaign is in collaboration with Russian scientists.

The Project provides to the ocean color community

satellite over-flight prediction support for SeaWiFS,

MODIS, MOS, OCI, OCM and OSMI, in addition to near

real-time SeaWiFS Level-1, 2 images in support of ongoing cruises.

b. Image browser may be used with SeaWiFS, MOS, OCTSGAC or OSMI data sets.

c. Sun photometer instrument pool deployments were made

for 14 MicroTops, 2 PREDE, 1 SIMBAD and 2

SIMBADA.

Outreach

a. The Project organized a special session at the Fall AGU

meeting (13-14 December, 2001) on “Calibration and

Validation Efforts Under Way by the Ocean Color

Missions”. We had a full day oral session and a 1⁄2 day

poster session. A substantial number of US and

international SIMBIOS investigators participated. For

more information see: http://agu.org/meetings

b. Project staff provided technical support for MODIS and

OSMI processing in the SeaWiFS Data Analysis System

(SeaDAS). The SIMBIOS and SeaWiFS Projects also

provided system administration, software maintenance,

and hardware upgrade support to the SeaDAS program.

c. This year, the SIMBIOS Project sent representatives to 12

national and international conferences and workshops, in

addition to MODIS and NIST workshops.

With these accomplishments, the SIMBIOS Project is

well positioned to undertake a number of new initiatives

in 2002. These include, but are not limited to, the

following:

• Continuing work on MODIS with the MODIS Oceans

Team and GSFC DAAC: match-up in situ parameter

comparison (L2); MODIS/SeaWIFS comparison (L3) and

coordination of the MODIS diagnostic data set.

• Continuing work on data merger by Project staff (L3):

Neural Networks and other techniques including

implementation of code from the Maritorena and Gregg

merging activities.

• Begin preparations for the ADEOS-II/GLI/POLDER and

ENVISAT/MERIS missions: sensor characterization and

data format information and planning of international

curises.

• Conclude the collaboration with KARI on the

KOMPSAT/OSMI calibration data and sensor

characterization

• Conclude the work with the MOS Science Team on the

use of MOS for linking the SeaWiFS with the ADEOS-I

OCTS and POLDER time series

• Rebuild and release the SeaBAM data set

• First release of the Global Marine Atmosphere Database:

QC and uncertainty

• Assist the NASA SeaDAS group with incorporating

OSMI processing capabilities into SeaDAS

• SIMBIOS NODC CD-ROM release in spring 2002.

REFERENCES

Bailey, S. W., J. Werdell and C. R. McClain, 2001:

Validation of Satellite-Derived Ocean Color: Theory and

Practice, EOS. Trans. AGU, 82(47), Fall Meet., Suppl.,

Abstract OS52A-0520, 2001.

Fargion, G. S., and J. L. Mueller, 2000: Ocean Optics for

Satellite Ocean Color Sensor Validation, Revision 2,
SIMBIOS Project Annual Report

4

NASA/TM-2000-209966, NASA Goddard Space Flight

Center, Greenbelt, MD, 184 pp.

Franz B. A. and J. M. Gales, 2001: A Long Term

Intercomparison of MOS and SeaWiFS, 4th Berlin

Workshop on Remote Sensing “5Years of MOS-IRS”, 9-

16.

Gales J., B. Franz and M. Wang, 2001: A Three Year

Intercomparison of Oceanic Optical Properties from MOS

and SeaWiFS, EOS. Trans. AGU, 82(47), Fall Meet.,

Suppl., Abstract OS52A-0513, 2001.

Knobelspiesse K., C. Pietras and G.S. Fargion, 2001:

MicroTops II Hand-held Sun Photometers Sun Pointing

Error Correction for Sea Deployment, EOS. Trans. AGU,

82(47), Fall Meet., Suppl., Abstract OS52A-0532, 2001.

Kwiatwska-Ainsworth, 2001; Merger of Ocean Color

Information of Different Spatial Resolution: SeaWiFs and

MOS,EOS. Trans. AGU, 82(47), Fall Meet., Suppl.,

Abstract OS52A-0514, 2001.

Mueller J.L., and G.S Fargion, 2002: Ocean Optics for

Satellite Ocean Color Sensor Validation, Revision 3,

NASA/TM-2002, NASA Goddard Space Flight Center,

Greenbelt, MD, in press.

Pietras C. et al. 2001: Aerosol Properties Derived from the

PREDE POM-01 Mark II Sun Photometer, EOS. Trans.

AGU, 82(47), Fall Meet., Suppl., Abstract OS52A-0531,

2001.

Wang, M., A. Isaacman, B.A. Franz, and C.R. McClain, 2002:

Ocean Color Optical Property Data Derived from OCTS

and POLDER: A Comparison Study, Appl. Opt. (in

press).

Werdell P. J., S. W. bailey and G.S. Fargion, 2001: The

Architecture and Utility of SeaBASS: the SeaWiFS BioOptical Archive and storage System, EOS. Trans. AGU,

82(47), Fall Meet., Suppl., Abstract OS52A-0533, 2001.

Table 1.1 SIMBIOS Project Personnel

Ewa Ainsworth

Sean Bailey*

Robert Barnes *

Patty Clow *

Giulietta Fargion

Gene Feldman *

Bryan Franz *

Joel Gales*

Lynne Hoppel *

Kirk Knobelspiesse*

Sung Lee *

Kathy Lingerfelt*

Charles McClain *

Gerhard Meister

Kevin Miller*

Fred Patt *

Christophe Pietras

India Robinson *

Paul Smith *

Judy Stubblefield*

Tamara Tucker *

Jeremy Werdell*

Bill Woodford*

SAIC

FutureTech Corporation

SAIC

NASA

SAIC

NASA

SAIC

FutureTech Corporation

NASA

SSAI Corporation

SAIC

NASA

NASA

FutureTech Corporation

FutureTech Corporation

SAIC

SAIC

NASA

SAIC

SAIC

SAIC

SSAI

FutureTech Corporation

* shared with SeaWiFS Project
SIMBIOS Project Annual Report

5

Chapter 2

SIMBIOS: Science Team and Contracts

Giulietta S. Fargion

 Science Applications International Corporation (SAIC), Beltsville, Maryland

2.1 SCIENCE TEAM

The Science Team is selected through a NASA Research

Announcement (NRA). Presently, NASA has had two NRA’s,

in 1996 and 1999. NASA HQ manages the process of team

selection, but the Goddard Space Flight Center (GSFC) NASA

Procurement Office handles the team contracts, work

statements and, if necessary, budget negotiations. The Project

funds numerous US investigators and collaborates with several

international investigators, space agencies (e.g., NASDA,

CNES, KARI, etc.) and international organizations (e.g.,

IOCCG, JRC). US investigators under contract provide in situ

atmospheric and bio-optical data sets, and develop algorithms

and methodologies for data merger schemes. NASA GSFC

Procurement requires formal evaluations for all contracts at

the end of each contract year. These evaluations are to go into

a database and are shared with the PI’s institution or upper

management.

The locations of specific SIMBIOS team investigations

(i.e., NRA-99) are shown in Figures 2.1. The international

ocean color community response for the NRA-99 was

overwhelming, with a total of 75 PI’s attempting to

collaborate with the Project group in twelve proposals. The

twelve international proposals cover topics ranging from

protocols, calibration-validation activities, atmosphericbiological algorithms, and data merging.

The SIMBIOS Science Team (NRA-96) meetings were

held in August 1997 at Solomons Island (Maryland), in

September 1998 at La Jolla (California) and in September

1999 at Annapolis (Maryland). Meanwhile, the SIMBIOS

Science Team’s (NRA-99) meeting will be held in January

2001 at GSFC in Greenbelt, Maryland and in January 2002 at

the Mariott (BWI), Baltimore, Maryland.

In general, all Science Team members, US and

international, were in attendance or were represented by one

of their staff. Also, the IOCCG and MODIS Teams are

invited, and most attended or sent representatives. During

each year the Project Office has fostered international

collaborations by hosting visiting scientists at GSFC. These

visits lasted from one week to several months. For

international participants, the Project provided partial or asneeded travel support to team meetings or to collaborate with

the project on specific topics such as calibration, and product

evaluations.

The fifth science team meeting (January 2002) was the

most attended, participation reached over 90 scientists (Figure

2.2). The team had a substantial US (SIMBIOS and MODIS

PIs) and international participation, with official

representation from OCTS, POLDER-I and II, MOS, MERIS,

GLI, and VIIRS misssions. During the meeting the team

addressed atmospheric correction and bio-optical algorithms, a

MODIS data merging plan for 2002, uncertainty budget and

sources from in situ observation, SeaWIFS re-processing,

protocols updates and international collaborations. The

agenda, presentations, minutes and recommendations are

posted at http://simbios.gsfc.nasa.gov/Info/

Chapters 4 to 21 contain the individual PI’s contributions

and describe the funded research topics, field studies

activities, and results of concluded research. These chapters

are reproduced as submitted with minimal editing by thr

Project Office.

2.2 CONTRACT OVERVIEW

The second-year of the SIMBIOS NRA-99 contracts

ended on November 30, 2000. The SIMBIOS Project

scheduled a telephone conference (telecon), of about 30-45

minutes, with each PI and other appropriate staff during the

month of October. Prior to the telecon, the SIMBIOS Project

reviewed each contract, the statement of work, and the agreed

to deliveries. The Project Office followed the same procedure

used in 1999 and coordinated an inside panel with key

contract and project personnel to perform an across-the-board

evaluation of all funded contracts (Table 2.1).

Table 2.1 Contract evaluation key personnel

Contracting Officer: Lynne Hoppel

Contracting Assistant: Kathy Lingerfelt

Resource/Financial Officer: Patty Clow

Manager , SIMBIOS Project: Giulietta Fargion

Manager, Office for Global

 Carbon Studies: Charles McClain

The four categories to be evaluated are suggested in the

“Evaluation of Performance” from the Federal Acquisition

Regulation (FAR) 42.15 and NASA FAR Supplement (NSF)
SIMBIOS Project Annual Report

6

1842.15 or NASA form 1680 used by GSFC. Under the

“quality” category the following are considered:

• data quality and completeness;

• ancillary information provided on the data (metadata);

• the data's usefulness in relation to SIMBIOS goals, i.e.,

calibration, validation, and algorithm development; and

• quality of technical reports.

The “time” category is a mixed bag, but is viewed with

respect to data and documentation (monthly and year-end

reports, and special topic publications) and delivery times.

Under the “other” category is considered:

• scientific publications and scientific achievements;

• science team collaboration and involvement; and

• other significant events occurring during the contract

period evaluated.

As a result of the formal evaluation and telecon with the

SIMBIOS PIs, all but two investigators were evaluated as

good or very good. The GSFC Procurement Office

implemented and executed 12 second-year options, closed one

contract, and gave a 1-month no cost extension to one

contract. Presently, the Project has in place 12 contracts, 3

interagency agreements and 2 NASA investigations. Further

details on the research status and progress reports can be

found on our web site at

http://simbios.gsfc.nasa.gov/status.html.

Figure 2.1: Global distribution of the NRA-99 selected SIMBIOS studies. United States (field): (1) Chavez; (2) Frouin; (3) Gao; (4) Harding; (5)

Miller; (6) Mitchell; (7) Morrison; (8) Nelson; (9) Siegel; (10) Spinhirne; (11) Stumpf; (12) Subramaniam; (13) Zalewski. United States

(theoretical, protocols, or team support): Gregg; Hooker; Maritorena; Mueller; Trees and Wang. International: Antoine; Bohm; Chen;

Deschamps; Fougnie; Fukushima; He; Li; Ishizaka; Kim; Kopelevich ; Lynch; Tang and Zibordi.
SIMBIOS Project Annual Report

7

Figure 2.2: SIMBIOS Science Team Meeting in Baltimore (January 15-16-17, 2002).
SIMBIOS Project Annual Report

8

Chapter 3

SIMBIOS Project Data Processing and Analysis Results

 Giulietta Fargion, Bryan Franz, Ewa Kwiatkowska-Ainsworth, Christophe Pietras and Paul Smith

Science Applications International Corporation (SAIC), Beltsville, Maryland

Sean Bailey, Joel Gales and Gerhard Meister

FutureTech Corporation, Greenbelt, Maryland

Kirk Knobelspiesse and Jeremy Werdell

Science Systems and Applications Inc., Greenbelt, Maryland

Charles McClain and Gene Feldman

NASA Goddard Space Flight Center, Greenbelt, Maryland

3.1 INTRODUCTION

The SIMBIOS Project is concerned with ocean color

satellite sensor data intercomparison and merger for biological

and interdisciplinary studies of the global oceans (Barnes et

al., 2000). Imagery from different ocean color sensors (OCTS,

POLDER, SeaWiFS, MOS and OSMI) can now be processed

by a single software package using the same algorithms,

adjusted by different sensor spectral characteristics, and the

same ancillary meteorological and environmental data. This

enables cross-comparison and validation of the data derived

from satellite sensors and, consequently, creates continuity in

ocean color information on both the temporal and spatial

scale. The next step in this process is the integration of in situ

ocean and atmospheric parameters to enable cross-validation

and further refinement of the ocean color methodology. The

SIMBIOS Project Office accomplishments during 2001 year

are summarized under (a) satellite data processing, (b) data

merging (c) SeaBASS database, (d) supporting services, (e)

sun photometers and calibration activities and (f) calibration

round robins. These accomplishments are described below.

3.2 SATELLITE DATA PROCESSING

3.2.1 Satellite Characterization

The SIMBIOS Project has worked closely with our

colleagues in the OSMI Program and with the instrument

manufacturer to define and assemble the instrument

performance characteristics required for the lookup tables for

the algorithms that determine OSMI top-of-the-atmosphere

radiances. Of special importance in this regard are the spectral

responses of the OSMI bands and the spatial co-registration of

the detectors. In addition, the Project has opened a

collaboration with the GLI Program.GLI is scheduled for

launch nor sooner than November 2002. The GLI/SIMBIOS

collaboration has centered on the characteristics of the GLI

scan mirror as a function of scan angle and the normalization

of detector-to-detector gain differences in the GLI bands.

Plans are underway for a cross-calibration of the GLI and

SeaWiFS near infrared bands, which are used for the

determination of the aerosol type and amount for the

atmospheric correction algorithms.

3.2.2 MOS Data Collection, Processing, and Distribution

Since February of 1999, the SIMBIOS project has been

operating a receiving station at NASA's Wallops Flight

Facility (WFF) to acquire data from the German Modular

Optoelectronic Scanner (MOS) onboard the Indian IRS-P3

spacecraft. When a pass is acquired at Wallops, the raw files

are transferred to the SIMBIOS project at NASA's Goddard

Space Flight Center via an automated FTP process. The raw

files are then converted to Level-0 format through a software

package provided by the Indian Space Research Organization

(ISRO). The resulting Level-0 files are made available to the

German Remote Sensing Data Centre (DLR-DFD) for archive

and distribution. In addition, the SIMBIOS project is

processing the data through Level-1B using the standard

software provided by the German Institute for Space Sensor

Technology (DLR-ISST) (Neumann et al., 1995). All data

processed by the SIMBIOS project is made available through

the MOS browse system at

http://simbios.gsfc.nasa.gov/oceancolor.html. The Level-1B

data can be processed to Level-2 using SIMBIOS-developed

software tools distributed through SeaDAS.
SIMBIOS Project Annual Report

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Comparison with SeaWiFS

The SIMBIOS project developed an automated process to

perform sensor-to-sensor intercomparisons using the online

archive of MOS data from Wallops and the SeaWiFS HRPT

data from the GSFC groundstation. MOS scenes are first

reprocessed to Level-1B to ensure the latest calibration has

been applied, and then the meta-data of each 200 x 200 km

scene segment is examined to determine the observation time,

geographic area, and percentage of visible ocean pixels. If the

scene segment contains at least 80% ocean pixels and no more

than 20% cloud pixels, the process proceeds to perform a

database search for potential SeaWiFS file matchups. Once a

valid SeaWiFS match is located, the area corresponding to the

MOS scene segment is identified in terms of pixel and scan

limits within the SeaWiFS scene, and both subscenes are

processed to Level-2 using MSL12.

Match-up results from this automated analysis are shown

in Figure 3.1. Each point in the scatter plot represents the

average water-leaving reflectance of MOS versus SeaWiFS

within each match-up scene, for the spectral channels near

412, 443, 490, 510, and 555 nm. The selected scenes represent

various locations off of the U.S. East Coast, for various times

throughout the years 1999 through 2001. These results

demonstrate that the Gordan and Wang atmospheric correction

scheme (Gordon and Wang, 1994), in conjunction with a

sensor cross-calibration, can provide comparable waterleaving reflectance retrievals from two independent sensors

observing at different times within the day. The last panel of

Figure 3.1 is a color-color diagram comparing a typical ocean

color band ratio between the two sensors. The band ratios are

in good agreement with an r2

 of 0.72, indicating that

comparable ocean color retrievals can be derived.

This work was presented at the 4th Berlin Workshop on

Ocean Remote Sensing in June of 2001, and a poster

presentation was made at the Fall meeting of the American

Geophysical Union in December of 2001.

Figure 3.1: Scene-averaged match-up results for MOS versus SeaWiFS, at each SeaWiFS band. The last panel shows

the comparison of band ratios, for a typical ratio used to characterize ocean color.
SIMBIOS Project Annual Report

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3.2.3 OCTS Calibration and Processing

On request by and in collaboration with NASDA, the

SIMBIOS Project aquired the entire OCTS GAC Level-1A

archive and processed the data through Level-3 using the same

software employed for SeaWiFS processing. The goal of this

effort was to enhance the consistency of the OCTS and

SeaWiFS global datasets by minimizing calibration and

processing algorithm differences between the two missions.

Like SeaWiFS, the OCTS data was vicariously calibrated to

MOBY (Wang et al., 2001). The Level-2 processing was

performed using MSL12, with algorithms and output formats

nearly identical to standard SeaWiFS processing, and Level-3

processing was performed using standard SeaWiFS space and

time binning software. The OCTS products generated by

SIMBIOS have been distributed to the Goddard DAAC.

Figure 3.2 shows a comparison of global monthly

chlorophyll concentration for both the NASDA and SIMBIOSderived products. The results indicate that, relative to NASDA

processing, the SIMBIOS-retrieved chlorophyll concentrations

are lower in high chlorophyll waters and higher in low

chlorophyll waters. This is primarily due to the change in

chlorophyll algorithms (NASDA's OCTS-C versus an OCTS

variant of O'Reilly's OC4 algorithm) and the use of an iterative

correction for non-zero water-leaving radiances in the near

infrared.

Comparison to SeaWiFS and Future Activities

Comparitive studies of OCTS and SeaWiFS products were

performed at several, relatively homogenous ocean regions. A

comparison of seasonal variability between the two missions

for two such regions in the northern and southern Pacific is

shown in Figure 3.3. The SeaWiFS results show very good

annual repeatability in the seasonal cycle, suggesting that these

regions are not significantly influenced by inter-annual events

such as El Niño/La Niña. The OCTS results for the same

region show similar trends, but the chlorophyll concentrations

are generally about 30% lower than SeaWiFS. This may be

due to differences in the vicarious calibration, as the MOBY

dataset from the OCTS mission time-frame was limited in

quantity and quality.

One possible solution to the vicarious calibration

differences between OCTS and SeaWiFS is to simply crosscalibrate the two instruments. This could be done by selecting

one or more regions of the ocean where the optical properties

are expected to be stable from year to year, and forcing the

retrieved OCTS water-leaving radiances to match the average

seasonal variability observed by SeaWiFS.

Another possible approach to cross-calibration or crossmission validation is to make use of the MOS instrument,

which spans the lifetimes of both the OCTS and SeaWiFS

missions. The MOS instrument has already been crosscalibrated to SeaWiFS (Wang & Franz, 2000), so it would be

interesting to see how well the oceanic optical properties

retrieved from MOS compare with OCTS. The SIMBIOS

Project has aquired a large set of MOS data from 1997, and the

Project has been collecting MOS data directly from the

spacecraft since February 1999 (Franz, 2000), so the data is

available and it is expected that this multi-instrument

comparison will be performed in the near future.

3.2.4 OSMI Calibration and Processing

On request by and in collaboration with the Korea

Aerosopace Research Institute (KARI), the SIMBIOS Project

began an evaluation of the calibration and data quality of the

Ocean-Scanning Multi-Spectral Imager (OSMI), which is

currently flying on KOMPSAT. OSMI is a whisk-broom

scanning Charge Coupled Device (CDD) with 192 detectors

oriented along track and six programmable spectral channels in

the visible and near infrared. The instrument has a ground

resolution of approximately 1 km with a swath width of 800 km.

OSMI was launched on 21 December 1999 into a 685 km sunsynchronous polar orbit with an ascending equatorial crossing

near 11:00 a.m. local time.

Using the pre-launch calibration and standard atmospheric

correction software developed for OSMI, it was found that the

sensor was not able to retrieve reasonable ocean optical

properties. To allow for meaningful comparisons between

OSMI and SeaWiFS, it was necessary to first modify the

SIMBIOS atmospheric correction software, MSL12, to accept

and process the OSMI data, thereby ensuring consistent and

proven algorithms. The SeaWiFS normalized water-leaving

radiance measurements and aerosol optical thickness retrievals

from a clear scene over Hawaii were then used as truth data to

enable a vicarious calibration of the 192 independent CCD

elements of each OSMI spectral band.

Comparison with SeaWiFS and Future Activities

Figure 3.4 shows averaged water-leaving radiance,

chlorophyll, and aerosol optical thickness retrievals from both

OSMI and SeaWiFS, for a 1-deg square region near Hawaii.

The black symbols are the four-year SeaWiFS record, and the

red symbols are the available OSMI scenes. These results

show that, using the standard SIMBIOS atmospheric

correction approach (Gordon and Wang, 1994) in conjunction

with an instrument cross-calibration, it is possible to retrieve

oceanic optical properties from OSMI which are in good

agreement with SeaWiFS, on average.

It remains to be seen whether the OSMI instrument is

stable over time. Artifacts such as detector striping and

banding are visible in the OSMI scenes, and they appear to

vary over time. A more detailed analysis will be required to

better characterize and remove these effects. The software

and calibration files created by the SIMBIOS Project have

been supplied to KARI, and it is expected that they will be

able to use these tools to advance their understanding of the

OSMI instrument and further improve the calibration.
SIMBIOS Project Annual Report

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Figure 3.2: May 1997 global monthly chlorophyll retrievals from NASDA version 4 (top) and SIMBIOS processing (bottom).
SIMBIOS Project Annual Report

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Figure 3.3: Temporal variability of retrieved chlorophyll concentrations from OCTS and SeaWiFS in the northern and

southern Pacific.
SIMBIOS Project Annual Report

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Figure 3.4: OSMI (red) and SeaWiFS (black) mean oceanic optical property retrievals from a 1x1-deg square box in the

vicinity of Hawaii.

3.3 DATA MERGING

The purpose of data merger activities undertaken by the

SIMBIOS Project is to create scientific quality ocean color

data sets encompassing measurements from past, present and

any future satellite missions. The most obvious benefit of the

data merger is improvement in spatial and temporal ocean

color coverage (Gregg at al., 1998) and increased statistical

confidence in extracted bio-optical parameters (Thiebaux and

Pedder, 1987; Thiebaux, 1973). A large variety of useful

multi-sensor applications can be implemented which will take

advantage of sensor-varying calibration/validation accuracies

and spectral, spatial, temporal, and ground coverage

characteristics. The Project already has experience with sensor

cross-calibrations; SeaWiFS nLw and aerosol optical

thickness were used to calibrate MOS Level-1B radiances

(Wang and Franz, 2000). In the near future a number of

merged ocean color products are expected to be produced,

including daily global chlorophyll maps at the highest feasible

spatial resolution using MODIS and SeaWiFS (initially, using

36km mapped data); regional and local products for a variety

of local applications; long-term time series using CZCS,

OCTS/POLDER, MOS and SeaWiFS/MODIS data; and

climatological data sets.

There are many difficulties associated with ocean color

data merger. Sensors have varying designs and characteristics.

They rely on different calibration approaches, processing

algorithms, and means of vicarious calibration. With new

sensors, like MODIS-AM, and the planned MODIS-PM,

MERIS, GLI, and POLDER-II, the science community needs
SIMBIOS Project Annual Report

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to rely on the calibration/validation quality of data products

generated by the respective science teams. However, before

merging data, the differences in standard products among

sensors need to be assessed. The same products can be derived

using different spectral bands which may or may not cause

noticeable incompatibilities. Data acquisition and retrieval are

not straightforward between the missions because of

differences in file formats, geometric projections, and limited

availability of subsampled data sets.

Fortunately, over the last year the SIMBIOS Project

Office has been addressing the data merger difficulties. It has

acquired expertise with ingesting and analyzing data formats

from different missions, including recent MODIS-AM data

sets. Software for uniform calibration and processing of

selected ocean color missions has been made available to

improve the level of compatibility among products. For

example, the entire data set of OCTS GAC has been

reprocessed and made available to the user community

through DAAC; MOS has been cross-calibrated with

SeaWiFS; SeaWiFS, CZCS, OCTS, and MOS are available

for processing and display via SeaDAS; and MODIS-AM data

can be also displayed in SeaDAS. The Project has been

operating a thorough ocean color validation program to

quantify the accuracies of the missions’ products in

comparison to in situ measurements. Cross-sensor

comparisons have been performed to evaluate mission product

differences. The most recent efforts concentrated on MODIS

and SeaWiFS matchups. Finally, the Project Office has

initiated research and development of methodologies for

generating merged multi-sensor ocean color products. The

main emphasis has been placed on defining the algorithms

which can uniformly overcome mission-specific parameters

and be applied to different products and sensors.

Data Merger Achievements

In the last year, the SIMBIOS Project Office has

developed a number of image processing and data fusion

methodologies and algorithms to gain expertise and meet the

goals of data merger. Three major issues which concern the

generation of multi-sensor ocean color products have been

addressed:

• Improvement of ocean color global coverage (MODIS

and SeaWiFS).

• Merger of ocean color data of different spatial resolutions

(MOS and SeaWiFS).

• Merger of satellite and in situ measurements (SeaWiFS

and CalCOFI).

Improvement of ocean color global coverage (MODIS and

SeaWiFS)

Common data formats and products have been identified

for MODIS and SeaWiFS. For comparisons and to perform an

initial merger of data from both missions, L3 binned daily

global products were used at 4.63km resolution. For

chlorophyll comparisons, the MODIS chlor_a_2 product was

applied because this represents the OC3M algorithm which is

most similar to the SeaWiFS OC4v4 algorithm (chlor_a

product). A number of comparisons between SeaWiFS and

MODIS nLw, chlorophyll, nLw ratios, AOT, and K490 were

made using MODIS version 3 data from DAAC and SeaWiFS

data processed by the Project using the operational algorithm.

Also, MODIS data were made available from the University

of Miami Remote Sensing Group which were processed using

upgraded algorithms and new MOBY stray light correction

values. To match the MODIS processing, SeaWiFS data sets

for these comparisons were compiled from the results of the

unofficial fourth reprocessing which also included stray light

corrections for MOBY. Software was developed for the

combined extraction and analysis of binned MODIS and

SeaWiFS files. MODIS and SeaWiFS comparisons were made

for overlapping bins for each day. Product differences were

evaluated using density scatter plots and statistics. The

matchups were made using both total global coverage data and

open-ocean/clear-atmosphere data. The open-ocean/clearatmosphere observations were analyzed separately to

eliminate ambiguous case 2 water and high AOT conditions.

Preliminary daily global matchups (December 4, 2000;

April 4, 2001; and June 10, 2001) show that recent MODIS

processing does not exhibit time-dependent trends. SeaWiFS

and MODIS products compare relatively well for nLw, nLw

ratios, K_490, and chlorophyll concentration. When

considered exclusively, open ocean and clear atmosphere

conditions show the same statistical trends as the entire global

data set. To better evaluate product correlations and

differences between the two missions, more analyses are

necessary with global and local coverage ranges and crossseasonal temporal scales. This work will continue in

cooperation with the MODIS Oceans Team, MODIS group at

GSFC and the University of Miami.

Over the last year, both the SIMBIOS Science Team and

the SIMBIOS Project Office have investigated a number of

merger methodologies which would increase ocean color

coverage (Maritorena et al., 2000). MODIS and SeaWiFS

daily global sets have been used in these investigations. The

algorithm proposed by the SIMBIOS Project Office is aimed

at producing global merged products of consistent accuracy

for all data bins or pixels (Reynolds, 1988; Barnes, 1964). The

method has to be effective in circumstances where there are

only a limited number of sensors with patchy global

distributions. The approach should alleviate sensor-to-sensor

differences in instrument design, characteristics, calibration

peculiarities, and data processing. The merger will incorporate

product accuracy levels defined by matchups with in situ

measurements. In practice, one of the goals is to eliminate

discontinuities in product values in areas where individual and

joint satellite coverages meet. Since algorithms which smooth

out the coverage conversions would produce uneven

accuracies for distant pixels or bins, an alternate method has

been proposed which reproduces the missing sensor data for

regions where only a single sensor coverage exists. This
SIMBIOS Project Annual Report

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approach consists of the mapping of one sensor’s data so as to

imitate data from the other sensor. Although the mapping can

be performed using linear or non-linear regression, the use of

an artificial neural network is preferred because any complex

mapping functions can be approximated using such a neural

network methodology. The mapping can be obtained using

data from overlapping bins from both sensors, e.g. MODIS

and SeaWiFS bins used in the matchups. The neural network

will map chlorophyll values from one sensor into chlorophyll

values from the other sensor given additional information on

nLw, quality control parameters, spatial and temporal

chlorophyll distributions, and possibly other products. The

neural network scheme can also be used to map nLw and other

data from one sensor into the other. The methodology enables

uniform processing irrespective of sensor and product

characteristics. The largest effort associated with this method

is the determination of an optimal set of input products and

parameters for the neural network (data mining) as well as

training the network on representative global and temporal

data sets so as to cover the widest range of chlorophyll

conditions. Once trained, the neural network mapping will be

efficient. The merger can then be performed as a weighted

average for each pixel/bin between the two sensor data sets

where the weights will be defined by confidence levels

attributed to both sensors by the matchups with in situ

measurements. The neural network mapping research is ongoing.

Merger of ocean color data of different spatial resolutions

(MOS and SeaWiFS)

During the last year, the SIMBIOS Project Office has

examined the feasibility of merging chlorophyll concentration

products from ocean color sensors of different spatial

resolutions for cases where there is overlapping ground

coverage for individual scenes. The prospect of enhancing

oceanic features in lower resolution imagery through the use

of higher resolution data has also been studied. This algorithm

is based on a signal processing approach —wavelet

multiresolution analysis — which enables an image to be

examined at different frequency or scale intervals (Mallat,

1989). The resolution of an image, which is a measure of

detail information in the scene, can be defined and changed by

a combination of high pass and low pass filtering operations.

The scale of an image is changed by downsampling and

upsampling operations. The high frequency, low-scale spatial

detail in higher resolution scenes is extracted using the high

pass filters of the wavelet transform and is combined with the

complete lower resolution image (Nunez et al., 1999).

Reversal of the transform increases the resolution and adds

high frequency variation to the lower resolution scene. This

process enables spatial resolution enhancement without

altering the average magnitudes of the ocean color values.

Because of this, the wavelet method is particularly useful

when the sensor qualities are different and the measurement

accuracy of the lower resolution sensor should be preserved.

The wavelet algorithm has been tested on SeaWiFS and

MOS imagery  a rare opportunity as these two missions

have been cross-calibrated, uniformly processed, and analyzed

for overlapping concurrent ground-coverage within the

SIMBIOS Project. SeaWiFS Level-2 scenes are binned at 1km

and MOS Level-2 scenes at 0.5km resolution. Bins are then

projected onto a rectangular latitude/longitude grid map to

facilitate image processing and to preserve spatial resolution

of the bins in the mapped image. Any missing grid points

caused by the mapping of spherical coordinates onto a flat grid

are approximated. The approximation uses a wavelet-based

iterative algorithm that minimizes high frequency anomalies

associated with the missing data. This preprocessing provides

data with the desired size and resolution for the wavelet

analysis. A single wavelet filtering of the MOS image then

extracts pixel-to-pixel spatial detail from MOS data and

subsamples the MOS scene by 2. The entire preprocessed

SeaWiFS scene then replaces the output of the MOS low pass

filter (which is at 1km resolution). An inverse wavelet

transform is applied to this result which produces an

enhanced, 0.5km resolution SeaWiFS image. To merge the

data sets, a weighted addition of the enhanced SeaWiFS image

and the original MOS scene is produced which depends on the

established relative accuracies of the products from each

instrument.

To validate the wavelet algorithm, the original MOS

scenes were compared against wavelet-enhanced SeaWiFS

scenes and SeaWiFS scenes which were bi-linearly

interpolated to the MOS resolution. The bi-linear interpolation

does not provide the benefits of the higher resolution feature

extraction which enabled SeaWiFS imagery to acquire spatial

detail inherent in MOS data. Quantitatively, the correlation of

interpolated SeaWiFS imagery with original MOS imagery is

considerably smaller (~10%) than the correlation for the

wavelet-enhanced SeaWiFS scenes. Qualitatively, the gain in

spatial detail obtained by the wavelet approach is

consequential and unique.

There have been some difficulties associated with the

application of the wavelet approach. Although the waveletmerged scenes appear sharper, there is a degree of highfrequency noise introduced from MOS which is peculiar to

this sensor's data. As it happens, wavelets also provide a

means for denoising speckled imagery and this has been

implemented as an option in the algorithm (Dondro, 1995).

This option is based on the soft-thresholding of wavelet

coefficients and is equivalent to removing Gaussian noise

from an arbitrary image. Manipulation of wavelet coefficients

causes undesirable ringing effects in images because of the

high frequency features. To limit this ringing, a selected

number of transformed solutions based on different wavelet

functions is averaged. Flags and masks of the ocean color

products from both sensors are also merged in the final

product.

In the future, it would be interesting to apply the wavelet

algorithm to the merger of overlapping scenes of MODIS and

SeaWiFS data so that SeaWiFS imagery could be enhanced by
SIMBIOS Project Annual Report

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the spatial detail contained in MODIS data. Also, a useful

experiment would be to combine MODIS ocean color

products at 1km resolution with high frequency spatial

information contained in MODIS high-resolution bands, such

as 500m or 250m.

Merger of satellite and in situ measurements (SeaWiFS and

CalCOFI)

An approach has been developed to merge Level-2 ocean

color data with in situ measurements. The major purpose is to

provide a utility to expose changes in remotely sensed

chlorophyll range and distribution when collected in situ

chlorophyll measurements are overlaid. The algorithm can be

used for specialized local applications and when merging data

from multiple sources, as for climatological data sets. The

method is based on the application of the wavelet transform

which spatially spreads in situ data point values onto

corresponding areas in satellite imagery. These areas are

defined by a radius of influence and depend on the in situ

measurement localization. The effect of in situ data points

changes with their distance from the center of the areas and

the scaling uses the Hann window function. Lower frequency

coefficients of the original ocean color subscenes are replaced

with the lower frequency coefficients of these subscenes

updated with the in situ data points (Starck and Murtagh,

1994). The wavelet forces the resulting satellite pixels to be

interpolations of in situ data points which, at the low

resolution, are distributed smoothly around their areas of

influence. Leaving the higher frequency coefficients

associated with the image unchanged preserves the original

high-resolution spatial variations within the areas of influence

and protects pixel-to-pixel data variabilities.

The radius of area of influence can be defined using

texture extraction. The more irregular the texture around the in

situ measurement point, the smaller the radius; the smoother

the texture, the bigger the radius. The merger is dependent on

the established relative accuracies assigned to in situ and

satellite data. A condition for merger is set as the maximum

time difference between the satellite and in situ observations.

Results of the satellite and in situ measurement merger

algorithm have been analyzed for the years 1997 and 1998

using SeaWiFS and California Cooperative Oceanic Fisheries

Investigation (CalCOFI) data sets. Although the largest time

difference between the SeaWiFS overflight and in situ data

collection ranges from +

3 hours to +12 hours, there were just

13 SeaWiFS files for which the merger could have been

performed, with a single maximum of three points per scene.

The low number of matchups was principally caused by the

presence of cloud cover. To limit the cases where small clouds

(a few pixels long) and other conditions cause ocean color

pixels to be masked out from the imagery, a gap-filling

algorithm has been designed and implemented. The algorithm

is based on the iterative reduction of total high frequency

content corresponding to missing pixels in the analyzed scene.

The frequency content of a pixel is established by inverting a

wavelet transform of a scene where the inversion is limited

only to the higher frequencies extracted by the forward

transform. The iterations are initialized by filling the gaps with

values corresponding to the lower frequency representation of

the scene. To eliminate local minima, a random perturbation is

introduced to the best gap pixel values which have been found

so far in the course of the algorithm. The gap-filling algorithm

has been implemented in combination with the satellite and in

situ measurement merger to eliminate small clouds within the

areas of influence of in situ data points. This contributed an

increase in matchups of around 10%.

Future Activities

In the last year, the SIMBIOS Project Office has gained

experience in a variety of different approaches to ocean color

data merger. For the next year, it is looking forward to

cooperating with the SIMBIOS Science Team, MODIS Team,

and DAAC on sensor intercomparisons, data merger, and

algorithm implementation. The merger emphasis will now be

placed on global ocean color missions such as MODIS and

SeaWiFS. The Project Office will work on defining an optimal

mapping algorithm for MODIS and SeaWiFS and creating

daily merged global chlorophyll products. In the future, other

products can also be considered like nLw at different spectral

bands and aerosol optical thickness. MODIS-PM, MERIS,

GLI and POLDER II data will also be investigated once they

become available from their respective ocean color teams.

3.4 SEABASS INTERFACE

The SeaWiFS Bio-optical Archive and Storage System

(SeaBASS) serves as a local repository for in situ data used in

a variety of scientific analyses, for example, satellite data

product validation, bio-optical algorithm development, and

data merger studies. Archived data include measurements of

apparent and inherent optical properties, phytoplankton

pigment concentrations, aerosol optical thickness, and other

related oceanographic and atmospheric data collected on

ships, moorings, and drifters. Additional information on

SeaBASS is provided in Werdell et al. (2000) and via the

World Wide Web at: http://seabass.gsfc.nasa.gov>.

SeaBASS contains data from over 750 cruises,

encompassing more than 25,000 data files. The volume of

archived data is rapidly increasing, as SIMBIOS US Science

Team members are contractually obligated to provide data

(McClain and Fargion 1999a and 1999b).

SeaBASS data files are flat (two-dimensional) ASCII text

with data presented in columns. Each file includes a series of

predefined metadata headers, which provide descriptive

information about the data, such as the names of the

contributor and experiment, the date and location, and other

ancillary data. All data files and related documents are stored

in a directory tree organized by contributor affiliation,

experiment, and specific cruise. Data are further archived
SIMBIOS Project Annual Report

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using a relational database management system (RDBMS).

The RDBMS is used to catalog and archive metadata and data

information from each data file, and to locate and retrieve

specific metadata and geophysical data information from the

full bio-optical data set. The standard file format and the

storage protocols did not change significantly during the past

year of the SIMBIOS effort. The SIMBIOS Project maintains

software, named FCHECK, to validate the format of SeaBASS

data files. Contributors are requested to use either the

electronic mail or File Transfer Protocol (FTP) version of

FCHECK to evaluate their data files prior to submission.

Once the files meet SeaBASS format requirements, the

contributor may submit their data and related documentation,

such as cruise reports and instrument calibration files, to the

SeaBASS Administrator via FTP. Additional information

regarding FCHECK and data submission protocols is available

online at <http://seabass.gsfc.nasa.gov/seabass_submit.html>.

Protocols for format validation and data submission have

not been altered over the past year. The full bio-optical data

set is available online via the SeaBASS Web site. Data are

located and retrieved using a series of online search engines,

listed and described in detail at:

<http://seabass.gsfc.nasa.gov/dataordering.html>.

Each search engine allows users to limit queries to

particular experiment, contributors, dates, locations, and data

types. Several additional search engines were developed

during the past year. In February 2001, a utility for retrieving

geophysical pigment values from the bio-optical data set,

named the SeaBASS Pigment Locator, was put online.

Similarly, in May 2001, a tool for retrieving geophysical sun

photometer data, named the SeaBASS Aerosol Locator, was

put online. To protect the publication rights of contributors'

data, full access to SeaBASS is limited to members of the

SIMBIOS Science Team and other NASA-funded researchers.

In July 2001, however, all data collected prior to 31 December

1999 were made available to the public. A password is

required to access more recent data. In December 2001, the

public data were released to NOAA's National Oceanographic

Data Center. The development of a CD-ROM version of the

full public bio-optical data set is planned for Spring 2002.

The procedures for the water-leaving radiance,

chlorophyll a, and aerosol optical thickness satellite-to-in situ

match-up analyses changed little during the past year of the

SIMBIOS effort. Information on these analyses may be found

in the SeaWiFS Postlaunch Technical Report Series Volume

10 (Bailey et al. 2000).

3.5 SUPPORT SERVICES

In an effort to improve the quality and quantity of

calibration and validation data sets, the SIMBIOS Project

offers several support services to field investigators. These

services include; scheduling of on-board LAC recording for

SeaWiFS; overflight predictions for operational sensors

(currently SeaWiFS, OCTS, MOS-B, MODIS, OCI, OCM and

OSMI); near real time SeaWiFS imagery for cruise locations;

and sunphotometer instrumentation from a pool of projectowned instruments. These services may be requested via the

World Wide Web at http://simbios.gsfc.nasa.gov. In return for

these services, the SIMBIOS Project requests that the field

investigators provide in situ validation data to the Project's

bio-optical archive, SeaBASS. Since January of 2001, the

SIMBIOS Project has supported 76 cruises (Table 3.1).

Scheduling SeaWiFS On-board LAC Recording

Since much of the world’s oceans are not covered by a

SeaWiFS HRPT station, high-resolution data may be recorded

onboard the SeaWiFS sensor. As a service to the scientific

community, the SIMBIOS Project in conjunction with the

SeaWiFS Project can schedule SeaWiFS onboard LAC for

cruises that occur outside HRPT coverage. SeaWiFS has the

ability to record a maximum of 10 minutes of high-resolution

data per downlink. Typically, a 30-second interval is allotted

for LAC target, which corresponds to 180 scan lines or

approximately 200 km along track at nadir. Detailed

information on LAC scheduling is available on the SIMBIOS

web site.

Overflight Predictions for Operational Sensors

For calibration and validation purposes, in situ

measurements should be made as close to the sensor overflight

time as is possible. To aid investigators in determining when

sampling should occur, the SIMBIOS Project offers overflight

predictions for all operational ocean color remote sensors.

Currently, the sensors supported are SeaWiFS, MOS-B, OCI,

MODIS, OSMI and OCM. Detailed information on overflight

predictions is available on the SIMBIOS web site.

Near Real Time SeaWiFS Imagery

In addition to providing predictions for satellite overflight

times, the SIMBIOS Project offers near real time imagery of

the operational SeaWiFS products in JPEG or GIF format to

cruises at sea. 'True color' images are in JPEG format, all

other products are in GIF format. These images provide field

investigators with additional information with which they may

maximize in situ sampling of transient oceanographic features.

The default specifications for the images provided include:

• available LAC, HRPT, and GAC;

• chlorophyll-a and pseudo-true color images;

• 2-degree box about a designated location or the entire

designated region;

• image width of 600 pixels;

• minimum percent valid chlorophyll pixels: 5%;

• images may be customized to best accommodate

individual investigator needs.

Detailed information on near real-time imagery is

available on the SIMBIOS web site.
SIMBIOS Project Annual Report

18

Table 3.1: SIMBIOS supported cruises with services provided.

Experiment name Investigator Period Services

 Railroad_Valley Edward Zalewski Jan 6 2001 Jan 8 2001 overflight

 Poseidon_2001 Thomas Martin Jan 8 2001 Feb 23 2001 image

 Bering_Sea_Cocc Peter Miller Jan 11 2001 Feb 18 2001 image, LAC

 Poseidon_2001 Thomas Martin Jan 13 2001 Feb 13 2001 image

 jan01sj Ajit Subramaniam Jan 23 2001 Feb 24 2001 image, LAC, overflight

 HOM3 Mike Mickelson Feb 1 2001 Jan 1 2002 image

 Bahamas_2001 Stanford Hooker Feb 1 2001 Mar 4 2001 image, overflight

 ANTXVIII_5 Andre Belem Feb 7 2001 May 7 2001 overflight

 AMLR2001 Mati Kahru Feb 8 2001 Mar 5 2001 LAC, overflight

 GasEx1 Francisco Chave Feb 10 2001 Mar 3 2001 LAC,image,overflight

 GASPROD Francis Gohin Feb 12 2001 Mar 6 2001 image

 ACE_ASIA Philip A. Durke Feb 20 2001 Feb 28 2001 image

 Lake_Tahoe Edward Zalewski Feb 20 2001 Mar 1 2001 overflight

 WestMed2001 Emanuele Bohm Feb 27 2001 Mar 22 2001 image

 SBC_LTER_01 Dave Siegel Mar 1 2001 Mar 29 2001 image, overflight

 WRIScS_Project Samantha Lavend Mar 1 2001 Sep 30 2001 image

 GasEx2 Francisco Chavez Mar 3 2001 Mar 10 2001 LAC, overflight

 htob Francisco Chavez Mar 13 2001 Mar 29 2001 LAC

 SF6_Eastern_Med Steve Groom Apr 1 2001 May 21 2001 image

 Railroad_Valley Edward Zalewski Apr 8 2001 Apr 12 2001 overflight

 Palmer_2001 Andre Belem Apr 12 2001 Apr 30 2001 image

 Bluefin_Gulf_of Andreas Walli Apr 12 2001 May 15 2001 image

 EN355_test Ru Morrison Apr 19 2001 Apr 23 2001 image

 IsoFix_II Mark Altabet Apr 25 2001 May 30 2001 image

 CLAMS_test William Smith Apr 26 2001 Apr 28 2001 image

 MBARI_ESP2001 Brian Schlining Apr 30 2001 May 30 2001 image, overflight

 ALKOR_159 Thomas Martin May 1 2001 May 13 2001 image

 Meteor_50_1 Thomas Martin May 1 2001 May 31 2001 image

 CoOP_WEST_Spring_2001 Raphael Kudela May 1 2001 Jun 19 2001 image

 NC_Ferry Varis Ransibrah May 10 2001 Jun 1 2001 overflight

 W0105C Ricardo Letelier May 15 2001 Jun 15 2001 image

 EN355 Ru Morrison May 30 2001 Jun 15 2001 image, overflight

 CLAMS William Smith Jun 7 2001 Aug 6 2001 image

 CLT2001 Glenn Cota Jun 11 2001 Aug 31 2001 overflight

 NC_monthly_ferr Varis Ransi Jun 11 2001 Jun 30 2001 overflight

 TAG_A_GIANT_200 Andreas Walli Jun 15 2001 Apr 25 2002 image

 Mucillagine_200 Vittorio Barale Jun 15 2001 Sep 15 2001 image

 DYNAS Thomas Ohde Jun 17 2001 Jun 23 2001 overflight

 Belgica_2001 Kevin Ruddick Jun 17 2001 Jun 23 2001 overflight

 jun01kn_1 Ajit Subramania Jun 26 2001 Jul 7 2001 overflight

 Pacific_Bluefin Kevin Weng Jun 27 2001 Aug 5 2001 image

 SedTrap2 Aurea Ciotti Jul 1 2001 Jul 10 2001 overflight

 NC_ferry_July Varis Ransi Jul 1 2001 Jul 31 2001 overflight

 SLAswarm Ru Morrison Jul 3 2001 Jul 15 2001 image

 CLT2001_1 Glenn Cota Jul 4 2001 Aug 14 2001 image

 jun01kn_2 Ajit Subramania Jul 6 2001 Jul 16 2001 LAC, overflight

 MELEE_V_CCGS Steven Wilhelm Jul 6 2001 Jul 20 2001 image, overflight

 Cornell_Worksho Bruce Monger Jul 7 2001 Jul 17 2001 image, overflight

 jun01kn_leg2 Ajit Subramania Jul 20 2001 Aug 18 2001 image, LAC, overflight

 COAS_COAST Ricardo Letelie Aug 1 2001 Aug 31 2001 image

 Cerebrus Will Aicken Aug 6 2001 Sep 14 2001 image

 PDC_Japan Craig Laben Aug 8 2001 Jan 31 2002 image

 PDC_Hawaii Craig Laben Aug 8 2001 Jan 31 2002 image

 PDC_Taiwan Craig Laben Aug 8 2001 Jan 31 2002 image

 PDC_Malaysia Craig Laben Aug 8 2001 Jan 31 2002 image

 PDC_Indonesia Craig Laben Aug 8 2001 Jan 31 2002 image

 PDC_Philippines Craig Laben Aug 8 2001 Jan 31 2002 image

 AFMA_Sulu_Sea Kathleen M. Sil Aug 10 2001 Aug 25 2001 LAC, overflight

 Zeeleeuw_2001 Kevin Ruddick Aug 17 2001 Sep 14 2001 overflight

 AMBITION Peter Miller Aug 23 2001 Oct 5 2001 image

 CLT2001 Glenn Cota Sep 1 2001 Dec 31 2001 overflight

 Lake_Tahoe_Sep2 Edward Zalewski Sep 7 2001 Nov 30 2001 overflight

 Post_CLAMS William Smith Sep 11 2001 Oct 5 2001 image

 CT010912 Ru Morrison Sep 12 2001 Sep 25 2001 image

 MERIS_Prof_Penc Herbert Siegel Sep 12 2001 Sep 19 2001 overflight

 BPA_NMFS_Septem Pablo Clemente- Sep 18 2001 Sep 30 2001 overflight

 EN361 Ru Morrison Sep 28 2001 Oct 20 2001 image, overflight

 McGillicuddy_Eddies_2001 Norm Nelson Oct 1 2001 Oct 16 2001 image

 CT011004 Ru Morrison Oct 4 2001 Oct 22 2001 overflight

 Russian_Cruise Oleg Kopelevich Oct 6 2001 Mar 6 2002 LAC, overflight

 DIAPALIS_1 Cécile DUPOUY Oct 22 2001 Nov 1 2001 image

 MELEE_VI Steven Wilhelm Nov 2 2001 Nov 11 2001 image

 Chalk-Ex_A Barney Balch Nov 9 2001 Nov 14 2001 image

 PACSEA_2_and_3 Kathleen Silvan Nov 13 2001 Nov 25 2001 LAC

 Chalk-Ex_B Barney Balch Nov 14 2001 Nov 18 2001 image

 DIAPALIS_2 Cecile Dupouy Nov 26 2001 Dec 23 2001 LAC, image
SIMBIOS Project Annual Report

19

3.6 SIMBIOS SUNPHOTOMETERS

AND CALIBRATIONS

Atmospheric correction of satellite radiances and, in

particular, estimation of aerosol effects on the upwelling

radiance at the top of the atmosphere is one of the most

difficult aspects of satellite remote sensing. Merging of

aerosol properties obtained from in situ observations with

these derived by sensor algorithms creates exceptional

opportunities to validate and improve the atmospheric

correction. There are many uncertainties associated with in

situ measurements themselves. These uncertainties include sun

photometer or radiometer calibration and operational

problems, inadequate manual handling, and cloud

contamination. When matching against atmospheric properties

obtained by a satellite sensor, additional uncertainties come

into play. These uncertainties may be caused by different

satellite and surface instrument viewing angles and by

observation acquisition time discrepancies. In the case of the

atmosphere, these uncertainties are considerable. Therefore,

exact calibration of sun photometers and radiometers is

essential, as well as the best possible (and uniform from

instrument to instrument) correction of obtained

measurements. Finally, in order to cross-validate the quality of

in situ data, as well as extract measurements of high stability

and confidence, and compare them against satellite sensor

estimates with the largest degree of certainty, having multiple

observations from different sun photometers and radiometers

is essential. An extended group of ground stations from

AERONET is now available for product validation. These

instruments are located at either coastal or island stations and

were operational for a reasonable length of time after

SeaWiFS went into operation. Table 3.2 provides the

AERONET station name, location (latitude and longitude),

and the corresponding responsible AERONET PIs. In 2000,

match-ups analysis between AOT data obtained from in situ

observations and satellite-derived AOT levels were conducted

using some of the sites listed in Tables 3.2. Match-ups have

been obtained for the SeaWiFS-derived aerosol properties and

AOT levels calculated from AERONET and SIMBIOS sun

and sky radiometer data. The study showed the need to

improve the cloud-screening algorithms. Efforts have been

made to refine the protocols and the cloud-screening

algorithms. Extensive efforts have been also made to associate

uncertainties with aerosol optical thickness measured by

various sun photometers that compose the SIMBIOS pool of

sun photometers. Based on Russel et al. (1993) and Eck et al.

(1999), the uncertainties for the Microtops and SIMBAD sun

photometers were estimated and are summarized on Table 3.3.

Pointing Error Screening

Once AOT values have been calculated, points

representing erroneous measurements must be removed. It can

be difficult to point a hand held sun photometer at the sun

accurately from a moving platform, like a boat at sea.

Measurements that were taken when the instrument was not

pointed at the sun will produce erroneous AOT values. A

poorly pointed instrument mistakenly records less than the full

direct solar radiance, so the computed AOT is much higher

than reality. This is a significant problem with the Microtops

II sun photometer, as its measurement cycle of about 3Hz is

not always fast enough to avoid ship motion (Porter et al.

2001). Figure 3.5 is an example of the manifestation of this

pointing error.

Table 3.2: AERONET sites used for aerosol matchups analyses.

Station Latitude Longitude AERONET

PI

Arica -18.47 -70.31 B. Holben

Ascension Isl. -7.98 -14.41 C. McClain*

Azores 38.53 -28.63 C. McClain*

Bahrain 26.32 50.50 C. McClain*

Bermuda 32.37 -64.70 B. Holben

Chinae 35.67 -117.74 B. Holben

Coconut Island 21.433 -157.79 C. McClain*

Dry Tortugas 24.60 -82.80 K. Voss &

H. Gordon

Kaashidhoo 4.97 73.47 B. Holben

Lanai 20.83 -156.99 C. McClain*

Midway Island 28.21 -177.38 B. Holben

Nauru -0.52 166.92 M. Miller

Puerto Madryn -42.79 -65.01 C. McClain*

Rottnest Island -32.00 115.30 C. McClain*

SanNicolas 33.26 -119.49 R. Frouin

Tahiti -17.58 -149.61 C. McClain*

Erdemli 36.56 34.25 C. McClain*

Wallops 37.94 -75.47 C. McClain*

& B. Holben

* SIMBIOS Project Office

Table 3.3: Calibration and AOT uncertainties for

various sun photometers.The master cimels are used

to transfer the calibration to other sun photometers.

The uncertainties given for the Cimel are from

Holben et al. 1998 and Eck et al. 1999

0

0

V

V

δ

δτ

Master Cimel 0.002-0.005 0.005

Standard

Cimel

0.01 0.01-0.02

MicroTops 0.015 0.015-0.02

SIMBAD 0.015 0.015-0.02

Two steps were taken to reduce the possibility of

recording erroneous measurements with the Microtops. First,

the measurement protocol was changed. Unlike the default

protocol, which saves the average of the 4 largest (out of 32)
SIMBIOS Project Annual Report

20

voltage values, the new protocol logs the largest single value

of 20 measurements. This has several advantages. The largest

voltage is the only value recorded, so the chance of keeping a

contaminated point is minimized. In addition, the total time

needed to make this measurement is smaller than with the

default protocol, so more measurements can be taken in a

short period of time. After the experiment, a post-processing

algorithm is applied. This algorithm calculates the coefficient

of variation (standard deviation divided by the mean, or CoV)

for each set of measurements in each band. If the CoV is

above an arbitrary threshold of 0.05 (expressed in terms of

optical thickness), the lowest voltage (highest AOT) value is

removed, and the CoV recalculated. This is repeated until it is

less than 0.05 or there are not enough points left to calculate

the standard deviation. The passed points are those that passed

this iterative process in all bands. Figure 3.6 is an example of

data gathered with this protocol, and which points remained

after the post-processing algorithm. A post processing

protocol was also developed to remove erroneous

measurements due to pointing problems from data that were

already collected with the old (4 out of 32) protocol. One of

the fields in a raw Microtops data file is the standard deviation

of the four largest measurements used for each data point.

Data whose standard deviation, in any band, is larger than the

instrument error (0.015, in optical thickness) is simply

removed from the data set. While this severely limits the data

available for analysis, it reduces the need for manual data

screening. The improved pointing error screening was

presented at the Fall AGU meeting in December 2001

(Knobelspiesse et al., 2001).

Cloud Screening

Several methods for removing cloud contaminated

measurements from sun photometer data have been

established. (Reynolds et al. 2000, Smirnov et al. 2000) These

cloud screening algorithms are based upon the assumption that

cloud contaminated data contains higher temporal frequencies

than cloud free data. Screening is performed using various

statistical or signal processing techniques that remove data

with high temporal frequencies. Established screening

algorithms are intended for specific instrument types, and

often utilize measurement characteristics unique to that

instrument. The SIMBIOS project gathers data from a variety

of sun photometer types, and is therefore interested in cloud

screening methods that perform universally for data from all

sun photometers. In 2001, software to perform two types of

cloud screening was written. Screening results from three sun

photometer data sets taken during ACE-Asia 2001 will be

compared during 2002, with the hope that a uniform

SIMBIOS cloud screening method will be found.Software was

written to perform the cloud screening established in Smirnov

et al. (2000) for the Cimel land based sun photometer. This

approach uses a mix of statistically based criteria and a second

derivative based smoothness criteria to remove cloud

contaminated data. The SIMBIOS project wrote software to

perform this type cloud screening on data from any sun

photometer. It is not known if this screening method will work

equally on data with different sampling rates than that

measured by the Cimel sun photometer.

Figure 3.5 is a plot of AOT values at 870nm gathered by three sun photometers during the INDOEX cruise in the Indian

Ocean on March 10th, 1999. Data in blue are from the Fast Rotating Shadowband Radiometer. Data in green were

gathered with the SIMBAD hand held sun photometer. Data in red were gathered with the Microtops II hand held sun

photometer. Note how much of the Microtops II values are higher than measurements made with other instruments at the

same time. This is due to pointing errors.
SIMBIOS Project Annual Report

21

Figure 3.6 shows data gathered during the CalCOFI cruise off California on July 22nd, 2001 with the new Microtops II

measurement protocol. Points in black are the results of the largest of 20 data gathering protocol. Points with red boxes

around them remained after the post processing routine. Note that the unnaturally large AOT values have been removed.

Wavelet based signal processing has the potential to

provide a screening method that directly addresses the main

cloud screening assumption – that cloud contaminated data

has higher temporal frequencies than cloud free data. Clouds,

as expressed in optical thickness signals, are non-stationary

(non-repeating) high frequencies. Wavelet analysis is intended

for non-stationary signals. Therefore, wavelets are potentially

more useful than other frequency analysis techniques, such as

Fourier or statistical analysis, that are intended for stationary

signals. (Ainsworth et al. 2001) However, the proper

frequencies to remove has yet to be established. Software was

written to perform wavelet screening using a given frequency

threshold. Software was also written to iteratively determine

the proper threshold frequency by comparing results with the

Cimel screening by Smirnov et al. (2000). Results from this

parameterization process are not yet validated.

The ACE-Asia cruise in the spring of 2001 provided ideal

data for validation and comparison of the above screening

algorithms. During this cruise, simultaneous measurements

were made with the Fast Rotating Shadowband Radiometer,

the SIMBAD hand held sun photometer and the Microtops II

hand held sun photometer. In addition, measurements were

made with a Micro-Pulse LIDAR (MPL). Normalized Relative

Backscatter (NRB) data from this instrument could provide a

valuable verification of the existence of clouds. Therefore,

MPL NRB data were archived in SeaBASS. In 2002, the

above data sets will be used to determine and verify a

SIMBIOS cloud screening routine.

Data Tracking Software

As the SIMBIOS instrument pool grows, tracking the

shipping, calibration and data processing has become

increasingly difficult. Several steps were taken to streamline

the management of the above tasks. First, the voltage to AOT

data processing was consolidated to use the same software on

data from various instruments. This insures that the calibration

coefficients and ancillary data are applied in a uniform

manner. Next, routines were created to automatically update

the calibration constants shown on the SIMBIOS web page, so

that the same set of current values are always used. To reduce

miscommunication, an email account was created to provide a

single sun photometery contact point for PI’s. Finally, a

management software package was created so track the

instrument requests, shipping, calibration and data processing.

This software produces plots on the SIMBIOS web site so that

everybody can see the instrument schedules.

Sensor Calibration

Seven Cimel sun photometers have been re-designed to

help the site managers and the Aeronet group maintain the
SIMBIOS Project Annual Report

22

instruments. The new design includes a new robot, new

connectors, and cables that make easier the exchange of parts.

Two new Cimels were bought and delivered to handle and

perform gradually the modifications to every sun photometer.

Fourteen Cimel sun photometers compose the SIMBIOS Pool

of instruments at this time; ten are deployed in the field of

which five need to be refitted; two are expected to be back for

calibration and redesign; the last four are ready to be

deployed. The Aeronet group is now in charge of the

maintenance and shipping of the SIMBIOS pool of Cimel sun

photometers.

Twelve Microtops, two SIMBADs and two PREDE Mark

II sun photometers were deployed in several cruises between

1998 and 2001. The data collected by the Project is available

at http://seabass.gsfc.nasa.gov/, where an interactive mapping

tool is provided to public users. Two new Microtops were

bought to support the expected growth of cruise experiments

and the expected filter degradation of some Microtops. Two

SIMBADAs, new versions of the SIMBAD with 11 channels,

have been delivered to the project in late 2001, calibrated at

GSFC, and are ready for deployment in 2002.

In 2000, the PREDE Company upgraded our pair of land

version PREDE to ship versions. The ship versions were

checked at Goddard Space Flight center in 2001. The PREDE

PS1000111 was used at GSFC and several modifications were

performed to improve the instrument water proofing. The

instrument was deployed during the ACE-ASIA experiment in

March and April 2001, while the PREDE PS110611 was

shipped back to Japan to repair the optical alignment. The data

analysis algorithm was delivered to the SIMBIOS Porject in

March 2001 and great efforts were made to implement and test

the code with the data collected at GSFC. The data collected

during ACE-ASIA and at GSFC have been analyzed and

presented at the AGU meeting in San Francisco in December

2001 (Pietras et al. 2001).

Sun Photometer Calibration: Non-Polarized Channels

The calibration of the sun photometers was described in

McClain and Fargion (1999). Details on operating the sun

photometers, calibration and the theoretical principles are

posted at http://simbios.gsfc.nasa.gov/Sunphoto

meters/calibration.html.

Twelve Microtops, two SIMBADs and two PREDEs

compose the SIMBIOS instrument pool. Two new Microtops

were added to account for the filter degradation noticed in

some instruments. Two SIMBADA, new versions of the

SIMBAD with 11 channel were delivered in late 2001 to

support the growth of the SIMBIOS PIs requests for above

water instruments. Each instrument is calibrated before and

after every cruise deployment. The processing code is

common to all sun photometers and is under development to

account for the two new SIMBADAs. This code allows the

cross calibration with a master Cimel instrument based on the

time to time voltage ratios (V0). Only clear days that show a

variation ∆V0/V0 less than 1% are retained as calibration days.

The time series of the cross calibration is reported in Table 3.4

for the Microtops #3773, the SIMBAD #972306 and the

PREDE #PS1000111 returned from the ACE-ASIA

experiment.

 Sun Photometer Calibration: Polarized Channels

Two polarized Cimel sun photometers are still deployed

in the field. The Aeronet group is now in charge of the

calibration of the polarized sun photometers. Instrument #162

has been refitted and deployed in Coconut Island (Hawaii).

Instrument #191 has been refitted and is ready to be deployed.

Above Water Radiometer Calibration

The SIMBAD and SIMBADA radiometers are also

designed to do above water measurements (in addition to

being a sun photometer). The optics and filters are the same

but the electronic gain is different. The calibration is generally

performed at GSFC using a 6’ or a 42” integrated sphere at

GSFC. Table 3.5 shows the reflectances per count obtained for

various calibrations performed since 1999 with two SIMBAD

instruments. Efforts are underway to estimate uncertainties of

the calibration coefficient determination based on the standard

deviation obtained from the measurements and based on the

accuracy of the integrated sphere calibration. The integrated

spheres are maintained and calibrated monthly by the

Calibration Facility group at Goddard Space Flight Center

(http://spectral.gsfc.nasa.gov). The two SIMBADA were also

calibrated since July 2001 using the 42” sphere. The analysis

code that process the sphere calibration is under development

to account for the new channels of SIMBADA and various

levels of lamps or various spheres.

New techniques have been also implemented to monitor

the behavior of the radiometers over time. The SeaWiFS

Quality Monitor (SQM) has been used used in 2001 to

monitor the SIMBAD 09; the time series is reported on

Table 3.5. Although the SQM is not a source as bright as the

6’ sphere, the relative comparison is easy to obtain and is

useful between two sphere absolute calibrations. In addition, a

Spectralon plaque has been bought and has been delivered to

the SIMBIOS Project in early spring 2001. The plaque is used

as a relative and absolute calibration device for the SIMBAD

and SIMBADA radiometers. The relative calibration consists

of determining the gain ratio (low/high) in each channel of the

two SIMBADA looking at the plaque illuminated by the sun

during a clear day. The results are summarized in Table 3.6.

The gain ratios time serie is helpful to observe a change in

some channels during a field experiment. Absolute calibration

that requires a precise estimate of the solar flux illuminating

the plaque needs to be develped.
SIMBIOS Project Annual Report

23

Table 3.4: Top of Atmosphere (TOA) signals (digital counts) and standard deviations for the

sun photometers determined by transfer calibration from a calibrated Cimel at GSFC between

August 1998 and September 2000.

MicroTops

03773

Cimel# 440 nm 500 nm 675 nm 870 nm

8/21/1998 37 1244 988 1218 824

6/9/1999 101 1238 987 1198 827

9/20/2000 37 1242 984 1194 817

7/6/2001 101 1224 980 1202 831

12/6/2001 101 1202 984 1208 825

SIMBAD

932706

440 nm 490 nm 560 nm 675 nm 870nm

8/21/1998 37 388591 479121 406870 421086 304820

12/14/1998 94 388269 473101 394874 410455 311944

9/23/1999 94 376205 464224 391526 416182 300000

10/28/1999 101 376820 462637 387034 410887 302475

3/6/2000 37 382815 465574 382168 408538 301005

PREDE

PS1000111

440 nm 500 nm 675 nm 870 nm 1020 nm

7/6/2001 101 1.480E-04 2.866E-04 3.643E-04 2.743E-04 1.530E-04

8/2/2001 101 1.446E-04 2.874E-04 3.668E-04 2.767E-04 1.530E-04

9/6/2001 94 1.451E-04 2.873E-04 3.646E-04 2.796E-04 1.552E-04

9/7/2001 94 1.460E-04 2.907E-04 3.635E-04 2.777E-04 1.542E-04

10/28/2001 94 1.817E-04 2.820E-04 3.557E-04 2.721E-04 1.533E-04

Table 3.5: Calibration coefficients (relectance/counts) for SIMBAD # 06 and 09 between 1999 and 2001.

SIMBAD

#972306 443nm 490nm 560nm 675nm 870nm

08-12-1999 3.8181 2.1922 2.4319 4.2292 8.0289

01-14-2000 3.7650 2.1339 2.3894 4.1096 7.6506

03-06-2000 3.6442 2.1186 2.4042 4.1324 7.7366

08-18-2000 3.7942 2.2376 2.5963 4.3958 8.2380

SIMBAD

#972309

08-12-1999 4.931 2.425 2.695 4.312 8.277

01-14-2000 5.0134 2.3451 2.6511 4.2599 7.9370

03-06-2000 4.7363 2.2771 2.6234 4.2219 8.0104

03-28-2001 4.6895 2.2732 2.6727 4.2406 8.5264

Table 3.6: Time series of the SIMBAD #972309 signals (digital counts) meaured with the SeaWiFS Quality Monitor.

SIMBAD

#972309

443nm 490nm 560nm 675nm 870nm

7/18/2001 1807.59 8038.90 7202.52 8959.86 16838.10

7/19/2001 1836.44 8189.62 7318.16 8891.35 16508.60

7/20/2001 1830.41 8176.76 7287.21 8846.80 16527.40

8/10/2001 1819.62 8189.30 7336.47 8880.20 16363.50

8/21/2001 1822.99 8185.61 7308.21 8874.63 16321.60

8/22/2001 1814.30 8165.62 7332.26 8891.08 16260.50

8/23/2001 1814.40 8188.15 7328.14 8890.44 16267.90

1/8/2002 1789.77 8278.85 7399.86 8786.97 15088.30
SIMBIOS Project Annual Report

24

Table 3.7: Gain ratios (Low/High) retrieved with both SIMBADA aiming the Spectralon plaque during a clear day.

SIMBADA #09 350nm 380nm 410nm 443nm 490nm 510nm 565nm 620nm 675nm 750nm 870nm

Spectralon

plaque 99.5 101.6 219.2 229.2 202.5 224.0 204.2 219.9 213.7 211.3 203.5

SIMBADA #07

Spectralon

plaque 100.6 100.4 226.5 220.0 205.5 230.3 219.8 213.9 219.0 230.2 228.3

3.7 CALIBRATION ROUND ROBIN

Calibration round-robin intercomparison experiments are

conducted by the SIMBIOS Project. The specific goals are

to:

• verify that all laboratories are on the same radiometric

scale

• detect and correct problems at any individual laboratory

in a timely fashion

• enforce the common use of calibration protocols

• identify areas where the calibration protocols need to be

improved

• document the calibration procedures specific to each

laboratory.

 The participating laboratories include academic

institutions, government agencies and instrument

manufacturers that either directly or indirectly contribute to

SeaBASS (see section 3.4). In the year 2001, the

participating laboratories of the first SIMBIOS Radiometric

Intercomparison SIMRIC-1 include:

• Naval Research Laboratory (NRL), Optical Sensing

Section, Washington, DC

• Scripps Institution of Oceanography, University of

California, San Diego, California

• Biospherical Instruments Inc., San Diego.

• The Institute for Computational Earth System Science

(ICESS), University of California at Santa Barbara,

California.

• HOBI Labs, Watsonville, California

• NASA Code 920.1 Calibration Facility, Greenbelt,

Maryland

• Satlantic Inc., Halifax, Canada

 The above laboratories were visited by SIMBIOS staff

with a NIST designed and calibrated radiometer, the

SeaWiFS Transfer Radiometer II (SXR-II), described in

Johnson et al., 1998a. The radiances produced by the

laboratories for calibration were measured in six channels

from 411 nm to 777 nm and compared to the radiances

expected by the laboratories. A NASA-TM (Meister et al.,

2002) documents the various calibration procedures in these

laboratories, evaluates the comparison results, and discusses

areas where the calibration protocols should be improved.

Furthermore, several characteristics of the SXR-II (e.g. its

radiometric stability and its field of view) and of the SQM-II

(e.g. the stability of its light output and the stability of its

internal detector) are described. The major result of the

SIMRIC-1 is that typically the SXR-II measured radiances

were higher than the expected radiances by 0 to 2 %, see

Figure 3.7. This level of agreement is satisfactory.

Furthermore, several issues were identified where the

calibration protocols need to be improved:

• the reflectance calibration of the reference plaques

• the possible use of an effective distance correction when

using the irradiance standards at distances greater than

the original 50 cm calibration distance

• all laboratories should adopt the new NIST 2000

irradiance scale as soon as possible.

The SXR-II was calibrated by NIST using SIRCUS in

December 2000. SIRCUS (Spectral Irradiance and Radiance

Calibrations with Uniform Sources) is a new calibration

facility that combines the use of tunable lasers and

integrating spheres. The radiometric stability of the SXR-II

from November 2000 to December 2001 was monitored by a

commercial portable light source, the Satlantic SeaWiFS

Quality Monitor II (SQM-II. A 2 % decrease was found for

the responsivity of the SXR-II channel 1 at 411 nm. Also, a

decrease of about 1 % is likely for SXR-II channel 6 at 777

nm. Both changes will be proved or disproved by the second

calibration of the SXR-II at NIST in December 2001. The

intensity of the SQM-II light output for the HiBank mode

increased by about 1 %, for a burning time of 113 hours,

which is a very good result. The internal detector of the

SQM-II measured an increase of about 9 %, the reason for

this discrepancy is unknown.

3.8 SIMBIOS COMPUTING

RESOURSES

The SIMBIOS computing facility consists of a variety of

equipment types. The main goal of the facility is to provide

computational support for the Project in the following areas:

• routine bulk data processing,

• large scale analysis and match-up between sensors,

• focused analysis on data subsets, and

• storage of large sensor data sets.
SIMBIOS Project Annual Report

25

Figure 3.7.: Comparison of the radiances measured by the SXR-II at the laboratories participating in the SIMRIC-1 and the

radiances expected by the laboratories. The differences (expected radiance minus measured radiance) are plotted in % as a

function of wavelength. The plot on the left shows the two laboratories on the NIST 2000 irradiance scale ('*' symbols for

NASA Code 920.1, '+' symbols for ICESS UCSB). The right plot shows the laboratories referenced to older NIST irradiance

scales ('*' symbols for NRL, '+' symbols for Biospherical, '+' symbols for Scripps, triangles for HOBI Labs, squares for

Satlantic). The difference at 411 nm for NASA Code 920.1 is due to the low responsivity of the Code 920.1 transfer radiometer

at that wavelength used to transfer the calibration from the NIST calibrated FEL to the Hardy integrating sphere, which was

measured by the SXR-II. At ICESS UCSB, calibrations are only performed up to 680 nm, so the strong disagreement at 777

nm does not affect their calibrations. At HOBI Labs, 2 further lamp standards were measured that showed disagreements of

more than 5 % (not shown). These 2 lamps were recalibrated after the SIMRIC-1; the new calibrations are in much better

agreement with the SXR-II measurements. The SXR-II measurements at Satlantic are affected by the decreases of responsivity

at 411 nm and 777 nm and are not corrected for in this plot.

The facility is co-located with the SeaWiFS Project onsite at the NASA Goddard Space Flight Center. This

arrangement benefits both projects, allowing sharing of

resources and costs for supporting equipment, network

infrastructure and the physical facility. Bulk data processing

and large-scale analysis tasks are performed by two Silicon

Graphics (SGI) Origin 2000 servers. Each system has 6

processors and approximately 3 gigabytes (GB) of RAM.

The influx of large data sets for additional sensors requires

increasing amounts of disk space, especially when performing

comparative analysis. The Data Processing server has nearly

100 GB of disk space and the Data Analysis server has almost

400 GB of total disk space. Long-term storage of data sets is

handled by the 6 TB tape library attached to the Data Analysis

server. Silicon Graphics O2 workstations now share software

development and focused analysis tasks with high

performance PCs running the Red Hat distribution of the

Linux operating system. The sizing of the workstations varies

with the task requirements. SIMBIOS is connected to the

Internet via a 100-megabit link, making network transfer of

large data sets more feasible. Internal connections are also

100 megabit for rapid transfer of data between the servers and
SIMBIOS Project Annual Report

26

the workstations. Data ingest for SIMBIOS is also done via

removable media including CD-ROM, 8 millimeter tape, 4

millimeter (DAT) tape, and DLT media up through DLT 7000.

Off-line media storage space is shared with the SeaWiFS

Project.

The group's work covered a variety of sensors over the

year, with a significant effort covering the OCTS data set.

Late in the year planning began on handling larger datasets

from other sensors. The ground station at Wallops Flight

Facility continues to receive MOS data downlinks, and the

facility continues to perform routine processing of MOS data

at the Greenbelt site. In support of these efforts, the facility

has experienced moderate growth over the past year. PC

workstations were added to the Project. Storage was

maintained at its current level, with tape archival used to make

room for additional data. With larger data sets on the horizon

for next year, the Project will be increasing its storage capacity

to meet the demand for space. Most space needs will be

addressed using relatively low cost network attached storage

(NAS) devices. Each server’s FibreChannel interface was

upgraded again to incorporate new functionality in

FibreChannel hardware. The new interfaces were required to

allow the servers to participate in the SeaWiFS Project Storage

Area Network (SAN). While the implementation of the

SeaWiFS high availability SGI server project has been

shelved, manual fail-over of hardware components has been

greatly enhanced with the use of SAN technology. Future

growth in server-based computing power will be required to

deal with large data set sensors. The Project will address these

requirements with rack-mounted Linux clusters, a method

which has proven highly successful for the SeaWiFS Data

Processing System.

REFERENCES

Ainsworth, E.J., C. Pietras, M. Miller, R. Frouin, and S.W.

Bailey, 2001: SIMBIOS Project Protocols for Processing

In Situ Aerosol Optical Thickness Measurements,

National Aeronautics and Space Administration, Goddard

Space Flight Center, Greenbelt, MD, Tech. Memo. 2001-

209982, 43-69.

Barnes, S.L. J., 1964: A Technique for Minimizing Details in

Numerical Weather Map Analysis, Applied Meteorology,

vol. 3, 396-410.

Bailey, S.W., C.R. McClain, P.J. Werdell, and B.D. Schieber,

2000: Normalized water-leaving radiance and chlorophyll

a match-up analyses, In SeaWiFS Postlaunch Calibration

and Validation Analyses, Part 2, NASA Tech. Memo.

2000-206892, S.B. Hooker and E.R. Firestone, Eds.

NASA Goddard Space Flight Center, Greenbelt,

Maryland, 45-52.

Donoho, D.L., 1995: De-Noising by Soft-Thresholding, IEEE

Trans. on Information Theory, vol. 41, no. 3, 613-627.

Eck, T.F., B.N.Holben, J.S.Reid, O.Dubovik, A.Smirnov,

N.T.O'Neill, I.Slutsker, and S.Kinne, 1999: The

wavelength dependence of the optical depth of biomass

burning, urban and desert dust aerosols, J. Geophys. Res.,

104, 31 333-31 350

Franz, Bryan A. 2000: Status of the MOS Ground Station at

NASA/Wallops, Proc. 3rd International Workshop on

MOS-IRS and Ocean Colour, Berlin, Germany, 21-23

April 1999.

Holben B.N., T.F.Eck, I.Slutsker, D.Tanre, J.P.Buis, A.Setzer,

E.Vermote, J.A.Reagan, Y.Kaufman, T.Nakajima,

F.Lavenu, I.Jankowiak, and A.Smirnov, 1998:

AERONET - A federated instrument network and data

archive for aerosol characterization, Rem. Sens. Environ.,

66, 1-16

Gordon, H. R., and Wang, M., 1994: Retrieval of waterleaving radiance and aerosol optical thickness over the

oceans with SeaWiFS: a preliminary algorithm, Appl.

Opt. 33, 443-452.

Gregg, W.W., W.E. Esaias, G.C. Feldman, R. Frouin, S.B.

Hooker, C.R. McClain, R.H. Woodward, 1998: Coverage

Opportunities for Global Ocean Color in a Multimission

Era, IEEE Trans. Geosci. and Remote Sens., vol. 36, no.

5, 1620-1627.

Knobelspiesse, K.D., C. Pietras, and G.S. Fargion, 2001:

Microtops II Hand-Held Sun Photometer Sun Pointing

Error Correction for Sea Deployment, Eos. Trans. AGU,

82(47), Fall Meet. Suppl., Abstract OS52A-0532.

Mallat, S.G., 1989: A Theory for Multiresolution Signal

Decomposition: The Wavelet Representation, IEEE

Trans. on Pattern Analysis and Machine Intelligence, vol.

11, no. 7, pp. 674-693.

Maritorena, S., D.A. Siegel, A.R. Peterson and M. LorenziKayser, 2000: Tuning of a Pseudo-analytical Ocean Color

Algorithm for Studies at Global Scales, AGU, OS12M05, Jan. 2000, San Antonio, Texas.

McClain, C.R. and G.S. Fargion, 1999a: SIMBIOS Project

1998 Annual Report. NASA Tech. Memo. 1999-208645,

NASA Goddard Space Flight Center, Greenbelt,

Maryland, 105 pp.
SIMBIOS Project Annual Report

27

McClain, C.R. and G.S. Fargion, 1999b: SIMBIOS Project

1999 Annual Report. NASA Tech. Memo. 1999-209486,

NASA Goddard Space Flight Center, Greenbelt,

Maryland, 128 pp.

Núñez, J., X. Otazu, O. Fors, A. Prades, V. Palà, R. Arbiol,

1999: Multiresolution-Based Image Fusion with Additive

Wavelet Decomposition, IEEE Trans. Geosci. and

Remote Sens., vol. 37, no.3, 1204-1211.

Pietras, C., R. Frouin, T. Nakajima, M. Yamano, K.D.

Knobelspiesse, J. Werdell, G. Meister, G. Fargion, C.

McClain, 2001: Aerosol Properties Derived from the

PREDE POM-01 Mark II Sun Photometer, Eos. Trans.

AGU, 82(47), Fall Meet. Suppl., Abstract OS52A-0531.

Porter, J.N., M.A. Miller, C. Pietras, and G. Motell, 2001:

Ship-based sun photometer measurements using

Microtops sun Photometers, J. Atmos. Ocean. Tech., 18,

765-774.

Reynolds R.W. 1988: A Real-Time Global Sea Surface

Temperature Analysis, Climate, vol. 1, 75-86.

Reynolds, R.M., M.A. Miller, and M.J. Bartholomew, 2000:

Design, operation, and calibration of a shipboard fast

rotating shadowband spectral radiometer. J. Atmos.

Ocean. Tech., 18, 200-214.

Russell, P. B., J.M. Livingston, E.G. Dutton, R.F. Pueschel,

J.A. Reagan, T.E. Defour, M.A. Box, D. Allen, P.

Pilewskie, B.M. Herman, S.A. Kinne, and D.J. Hofmann,

1993: Pinatubo and pre-Pinatubo optical depth spectra:

Mauna Loa measurements, comparisons, inferred particle

size distributions, radiative effects, and relationship to

lidar data, J. Geophys. Res., 98, 22,969-22,985.

Smirnov, A., B.N.Holben, T.F.Eck, O.Dubovik, and

I.Slutsker, 2000: Cloud screening and quality control

algorithms for the AERONET data base. Rem. Sens.

Environ., 73, 337-349.

Starck J. L. and F. Murtagh, 1994: Image Restoration with

Noise Supression Using The Wavelet Transform, Astron.

Astrophys., vol. 288, pp. 342-348.

Thiebaux H.J and Pedder, 1987: Spatial Objective Analysis,

Academic Press.

Thiebaux, H. J., 1973: Maximally Stable Estimation of

Meteorological Parameters at Grid Points, J. Atmospheric

Science, vol. 30, 1710-1714.

Wang M. , S. Bailey, C. Pietras, C.R. McClain, and T. Riley,

2000: SeaWiFS Aerosol Optical Thickness Matchup

Analysis, NASA Tech. Memo. 2000-206892, In McClain,

C.R., E.J. Ainsworth, R.A. Barnes, R.E. Eplee, Jr., F.S.

Patt, W.D. Robinson, M. Wang, and S.W. Bayley,

SeaWiFS Postlaunch Calibration and Validation analyses,

Part 1, S.B. Hooker and E.R. Firestone, Eds, NASA

Goddard Space Flight Center, Greenbelt, Maryland.

Wang, M., Isaacman, A, Franz, B. A. 2001: Ocean Color

Optical Property Data Derived from OCTS and POLDER:

A Comparison Study, Appl. Opt., in press.

Wang, M. and Franz, B. A., 2000: Comparing the ocean color

measurements between MOS and SeaWiFS: A vicarious

intercalibration approach for MOS, IEEE Trans. Geosci.

Remote Sensing, Vol. 38, No 1, 184-197.

Werdell, P.J., S.W. Bailey, and G.S. Fargion, 2000: SeaBASS

data protocols and policy, In Ocean Optics Protocols for

Satellite Ocean Color Sensor Validation, Revision 2,

NASA Tech. Memo. 2000-209966, G.S. Fargion and J.L.

Mueller, Eds. NASA Goddard Space Flight Center,

Greenbelt, Maryland, 170-172.
SIMBIOS Project Annual Report

28

Chapter 4

Adaptation of a Hyperspectral Atmospheric Correction Algorithm

for Multi-spectral Ocean Color Data in Coastal Waters

Bo-Cai Gao, Marcos J. Montes, and Curtiss O. Davis

Remote Sensing Division, Naval Research Laboratory, Washington, D.C.

4.1 INTRODUCTION

This SIMBIOS contract supports several activities over its

three-year time-span. These include certain computational

aspects of atmospheric correction, such as the modification of

our hyperspectral atmospheric correction algorithm for various

multi-spectral instruments, such as SeaWiFS, MODIS, and

GLI. Additionally, since absorbing aerosols are becoming

common in many coastal areas, this grant supports the

calculation and incorporation of various absorbing aerosol

models into tables used by our atmospheric correction

algorithm. Finally, we will use MODIS data to characterize

thin cirrus effects on aerosol retrieval.

4.2 RESEARCH ACTIVITIES

The research activities during the first year of this

contract were mostly concerned with modifications of our

hyperspectral atmospheric correction algorithm (Gao et al.

2000; Montes et al. 2001) to versions compatible with MODIS

and SeaWiFS. Our first concern was getting the algorithms to

compile and run successfully; a secondary concern was to

ensure that they are executing in a reasonable time. Each

version was compiled on both SGI and Linux workstations.

The Linux version is at the SeaWiFS project office for further

testing. Some testing has been done as part of an

AVIRIS/MODIS/MOBY intercalibration. In preparation for

calculating lookup-tables with various dust aerosol models, we

applied for and received computer time on the Naval Research

Laboratory’s High Performance Computer system.

4.3 RESEARCH RESULTS

The modification of the algorithms has proceeded along

two lines. For hyperspectral data using normal platforms, in

the past it has been sufficient to assume single solar and

observational geometries. This allows us to perform one major

interpolation over all the geometrical quantities in our lookup

tables before performing atmospheric correction. This is not

practical with both MODIS and SeaWiFS since their ground

swath widths are very large. For these versions, we have

currently implemented averaging over a user-specified box

when calculating the angular values used for the atmospheric

correction. The box sizes can be specified in both the x and y

directions, and the solar and view angles are averaged. These

averaged angles are used to interpolate the look-up tables.

Finally, the atmospheric correction is performed for each pixel

in the box. We have also investigated a pure pixel-by-pixel

atmospheric correction code. This necessarily executes more

slowly than the box versions because a separate interpolation

across the angular directions of the lookup tables must be

performed for each pixel.

We have processed a few scenes of MODIS and SeaWiFS

with the box versions of the atmospheric correction code.

Some studies have been done for areas near the MOBY buoy.

In particular, we have analyzed some scenes from April, 2000

when AVIRIS flew under MODIS and SeaWiFS. We

developed software to navigate the AVIRIS scene in order to

find locations in the AVIRIS and MODIS scenes with similar

view and solar geometry. Additionally, it was necessary to

correct the AVIRIS measurements for both absorption and

scattering effects from AVIRIS’approximately 20 km altitude

to the top of the atmosphere. We find that near 0.47 micron,

the observed reflectance (corrected to the top of the

atmosphere) for AVIRIS is 9% greater than that measured by

MODIS; at 0.747 micron the difference is about 8%, with the

MODIS values being greater than those of AVIRIS (see

Figures 4.1 and 4.2).

A final comparison is performed between the waterleaving reflectance from MOBY (Clark & Flora 2000) and

that calculated by atmospheric correction for AVIRIS and

MODIS. We find that MOBY agrees quite well with MODIS.

However, the aforementioned differences between AVIRIS

and MODIS lead to large differences in the derived waterleaving reflectance for AVIRIS and MOBY. There appear to

be two reasons for this effect. First, the 0.747 micron band

was used to determine the aerosol model and type. The lower

AVIRIS value implies a different aerosol model is being

chosen for the AVIRIS atmospheric correction – one with
SIMBIOS Project Annual Report

29

lower optical depth, and therefore less aerosol scattering to

remove than was indicated by the MODIS measurements.

Second, AVIRIS’s greater signal in the blue is subtracted out

less. Thus, the atmospherically corrected signal from AVIRIS

is much different than that of MODIS. Having said all this, we

are still working with only this single AVIRIS underflight of

MODIS, and with the initial calibration information we

received for MOBY, MODIS, and AVIRIS. These results may

change somewhat as we continue the testing of our software

and as we obtain better calibration information for each of the

instruments.

4.4 CONCLUSIONS

We have made progress in modifying our atmospheric

correction algorithm to be used with multi-spectral data from

MODIS and SeaWiFS. “Box” versions of these algorithms

have been successfully compiled and run on two platforms.

Initial results are promising, but in some instances we need to

determine final calibrations status for MOBY, MODIS, and

SeaWiFS data as we continue our testing. In the following

year we intend to begin calculations of tables with various

dusts, in addition to finishing continuing intercomparisons of

data from MOBY, MODIS, SeaWiFS, and AVIRIS.

REFERENCES

Clark, D. and S. Flora, 2000. Personal communication.

Gao, B.-C., M.J. Montes, Z. Ahmad, and C.O. Davis, 2000.

Atmospheric correction algorithm for hyperspectral

remote sensing of ocean color from space. Appl Opt.,

39(6), 887-896.

Montes, M.J., B.-C. Gao, and C.O. Davis. 2001: A new

algorithm for atmospheric correction of hyperspectral

remote sensing data. SPIE, 4383, 23-30.

Figure 4.1: Top of the atmosphere reflectance observed near the MOBY site by AVIRIS (dashed line) and MODIS (open

triangles). Note that while the radiance measurements are generally in good agreement, the AVIRIS radiance near 0.4

micron is significantly less than radiance observed by MODIS. The AVIRIS radiance at 0.747 micron is also about 10% less

than that measured by MODIS. The solid line with solid diamonds is the observed atmospheric reflectance before being

transferred to the top of the atmosphere.
SIMBIOS Project Annual Report

30

Figure 4.2: Derived water-leaving reflectance spectra from AVIRIS and MODIS (open triangles), compared to MOBY

(open circles, small dashes) measurements. The solid line is AVIRIS corrected using the two MODIS bands near 0.75

micron and 0.865 micron to determine the atmospheric correction. The long-dashed line is the atmospheric correction

derived using four window channels longer than 1 micron.

This research was supported by the SIMBIOS NASA interagency

agreement # S-44797-X

PRESENTATIONS

Gao, B. C., M.J. Montes, and C.O. Davis, 2001: Development

of an atmospheric correction algorithm for remote sensing

of coastal waters. Presented at the SIMBIOS Science

Team Meeting, Greenbelt.

Davis, C.O., B.-C. Gao, M.J. Montes, G. Feldman, B. Franz,

F. Patt, and R. Stumpf, 2001: Atmospheric Correction of

Ocean Color Data at the MOBY site in Hawaiian Waters.

Presented at the Tenth JPL AVIRIS Science and

Applications Workshop, Pasadena.
SIMBIOS Project Annual Report

31

Chapter 5

Bio-Optical Measurements in Upwelling Ecosystems in Support of

SIMBIOS

Francisco P. Chavez, Peter G. Strutton, Victor S. Kuwahara and Eric Drake

Monterey Bay Aquarium Research Institute, Moss Landing, California

5.1 INTRODUCTION

The equatorial Pacific strongly influences global

biogeochemistry, due to the upwelling which spans more than

a quarter of the earth’s circumference. This upwelling system

has significant implications for global CO2 fluxes (Tans et al.,

1990; Takahashi et al., 1997; Feely et al., 1999), as well as

primary and secondary production (Chavez and Barber, 1987;

Chavez and Toggweiler, 1995; Chavez et al., 1996; Dugdale

and Wilkerson, 1998; Chavez et al., 1999; Strutton and

Chavez, 2000). In addition, this region represents a large

oceanic province over which validation data for SeaWiFS are

necessary. This project consists of a mooring program and

supporting cruise-based measurements aimed at quantifying

the spectrum of biological and chemical variability in the

equatorial Pacific and obtaining validation data for SeaWiFS.

Since 1997, the MBARI equatorial Pacific program has

demonstrated ability to:

• obtain high quality, near real time measurements of ocean

color from moored platforms in the equatorial Pacific.

• process these data into files of the required format for

routine ftp to the SeaBASS database.

• obtain supporting ship-based optical profiles (including

water leaving radiance, Lwn) and pigment concentration

measurements, also for submission to SeaBASS.

• process and interpret the time series, satellite and shipbased data in order to quantify the biogeochemical

processes occurring in the equatorial Pacific on time

scales of days to years (Chavez et al., 1998; Chavez et al.,

1999; Strutton & Chavez, 2000; Strutton et al., 2001).

5.2 RESEARCH ACTIVITIES

Moorings

Chavez et al. (1998; 1999) and McClain and Fargion

(1999, p.44) describe the configuration of the MBARI biooptical and chemical instruments deployed at 0°, 155°W and

2°S, 170°W; two of the 70 moorings which form the Tropical

Atmosphere Ocean (TAO) array.

From these locations daily local 10 am and noon (ie

approximate time of MODIS and SeaWiFS overpasses,

respectively) bio-optical and chemical data are transmitted via

service ARGOS in near real time to MBARI, and then

displayed on the web at:

http://bog.shore.mbari.org/~bog/oasis.html.

Higher frequency, publication-quality data (15-minute

intervals) are recovered at approximately six month intervals,

and sent to the SeaBASS database after processing and quality

control. Derived products, such as water leaving radiance

(Lw), and remote sensing reflectance (Rrs) are included in

these data files for validation efforts. In the past, Lw has been

calculated via three different methods (McLain and Fargion,

1999b), but is currently calculated only as follows. The diffuse

attenuation coefficient (Kλ) over the upper 20m of the water

column is calculated using Ed3m+ and Ed20m-, then using this

Kλ, Lu20m is extrapolated back to just below the surface. This

parameter (Lu0m-) is then multiplied by 0.544 to account for

transmission across the air-water interface to obtain Luom+. Of

the three methods previously used, this has been shown to be

the most reliable, because the 3m+ and 20m- instruments are

less susceptible to fouling than the OCR-100s moored at

~1.5m. However, the efficacy of this method is currently

being assessed, and we hope to make improvements in the

near future.

During 2001 our data processing, quality control and data

provision capabilities have improved, and the majority of the

mooring data can now be viewed on the web at:

http://www.mbari.org/bog/Projects/EQPAC/eqpac_qc/eqpac_

qc.htm

The following quality control procedures are currently

applied to our data:

• Measured surface-incident irradiance (Es) must be less

than 1.15 times modeled, clear-sky Es (Frouin, 1989).

• Kλ must be greater than that of pure water (Morel, 1988).

• OC2V2 chlorophyll is calculated for the Rrs ratios of

412/555, 443/555, 490/555, and 510/555 and the
SIMBIOS Project Annual Report

32

coefficient of variance is calculated for each sample.

Coefficients of variance less than 0.4 are acceptable.

• Time series of all parameters at all wavelengths for all

individual instrument deployments are plotted together as

one long time series to identify discontinuities between

deployments, due to problems such as poor calibrations.

This is sometimes identified by normalizing specific

wavelengths against others. In coming years we hope to

continue improvement of these criteria and use the results

to perhaps modify mooring and instrument

configurations.

With the buoy design and data acquisition protocols now

proven in the field, we are making significant improvements

to the quantity and quality of optical data collected during this

funding period. This will amount to augmenting the discrete

wavelength instruments with hyperspectral, optical fiber

packages. We anticipate these configuration changes will

produce data of significantly higher quality for almost the

same cost as the existing discrete-wavelength instruments. In

2000 we deployed a HOBILabs HydroRad 4 (HR4) system at

the M1 mooring in Monterey Bay. In October 2001, we

deployed a HOBILabs HR3 at both equatorial Pacific

moorings: 0° 155°W and 2°S 170°W. This instrument will

provide hyperspectral data consisting of:

• downwelling irradiance above the surface (3m)

• downwelling irradiance and upwelling radiance at 10m

depth.

The Hydrorad data are available in programmable bin

sizes (highest resolution 0.37nm), over the range ~300 to

850nm, but for deployment in the equatorial Pacific we have

binned the data to ~2nm resolution. We also anticipate

improved fluorescence measurements with newly deployed

HOBILabs Hydroscat 2 (HS2) instruments fitted with new

copper anti-fouling shutters. The HS2 measures backscatter

and fluorescence at two wavelengths. With the deployment of

these new instruments, in particular the hyperspectral

radiometers, we can better support new and forthcoming ocean

color missions such as MODIS and GLI and the development

of ocean color algorithms that will go beyond chlorophyll.

Optical profiling measurements

On mooring maintenance cruises, optical profiles of the

upper 100 to 200 m of the water column are performed, daily

when possible, close to local noon. The instrument used is the

Satlantic SeaWiFS Profiling Multispectral Radiometer

(SPMR). Profile data are processed using Satlantic’s ProSoft

software, and a suite of derived products, including diffuse

attenuation coefficients, water leaving radiances (Lwn) and

light penetration depths are obtained. Parameters of interest

(mostly Lwn) are provided to NASA post-cruise, and the

profile data are archived at MBARI along with existing optical

profiles from almost every oceanic province.

In situ measurements

Table 5.1 summarizes the cruises undertaken by MBARI

this fiscal year in support of SIMBIOS. Essentially, the cruisebased measurements consist of chlorophyll (using the

fluorometric method described by Chavez et al. (1995) and

nutrient profiles (8 depths, 0-200m) obtained at CTD stations

between 8°N and 8°S across the Pacific from 95°W to 165°E.

On selected cruises (the 155°W and 170°W meridional

transects), HPLC samples are collected and productivity (14C, 15N) measurements are also performed. These data are

archived at MBARI and the pigment data provided to the

SeaBASS database for algorithm development. More recently

we have measured colored dissolved organic matter (CDOM)

on our cruises.

5.3 RESEARCH RESULTS

Biogeochemical cycles

Several publications describing ecosystem variability in

the equatorial Pacific have been produced under MBARI’s

SIMBIOS funding. Chavez et al. (1998) used mooring data

from 0°, 155°W to describe the biological-physical coupling

observed in the central equatorial Pacific during the onset of

the 1997-98 El Niño. Chavez et al. (1999) combined the

physical, biological and chemical data from moorings, ships

and SeaWiFS to provide a comprehensive view of the

ecosystem’s response to the extreme physical forcings

experienced during the 1997-98 El Niño. Strutton and Chavez

(2000) summarized the in situ cruise measurements spanning

the period from November 1996 to December 1998, and used

these data to describe the perturbations to chlorophyll,

nutrients and productivity during the same time period.

Strutton et al. (2001) used time series from smaller Satlantic

bio-optical packages and SeaWiFS imagery, to quantify the

extreme anomalies in chlorophyll associated with the passage

of tropical instability waves (TIWs) during the second half of

1998 and 1999. These data not only quantified the magnitude

of the chlorophyll anomalies observed, but also helped to

elucidate the mechanisms potentially responsible for the

concentration of chlorophyll in association with TIWs.

In addition to the manuscripts just described, Chavez et

al. (2000) documented the design, and demonstrated the

efficacy of the shutter mechanism which protects the 20m

radiometer on the moorings from fouling. Further, we have

submitted two abstracts to 2001 Fall AGU and are working on

the mooring chapter for the 3rd revision of the Mueller

protocols TM. Other manuscripts currently in preparation

include a book chapter for submission to a CRC Press

publication (Strutton et al. In prep), a paper describing the

evolution of the 1998 La Niña blooms (Ryan et al, in

revision), and a manuscript describing the biological

component of the equatorial Pacific heat budget (Strutton and

Chavez, in revision).
SIMBIOS Project Annual Report

33

EP1

0.0

0.3

0.5

0.8

1.0

1.3

1.5

1.8

2.0

2.3

2.5

1996 1997 1998 1999 2000 2001

Lu412

Lu443

Lu490

Lu510

Lu555

EP2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1996 1997 1998 1999 2000 2001

Lu412

Lu443

Lu490

Lu510

Lu555

Year

Figure 5.1: The time series of the upwelling radiance, Lu [μW cm-2 nm-1 sr-1], derived from the MBARI optical

instruments on the TAO moorings at 0° 155°W (EP1) and 2°S 170°W (EP2).

Lu [

μW cm-2 nm-1 sr-1]
SIMBIOS Project Annual Report

34

EP1

0.0

0.5

1.0

1.5

2.0

2.5

1996 1997 1998 1999 2000 2001

Lwn412

Lwn443

Lwn490

Lwn510

Lwn555

EP2

0.0

0.3

0.5

0.8

1.0

1.3

1.5

1.8

1996 1997 1998 1999 2000 2001

Lwn412

Lwn443

Lwn490

Lwn510

Lwn555

Year

Figure 5.2: The time series of normalized water-leaving radiance, Lwn [μW cm-2 nm-1 sr-1], derived from the Lu and

Kd data obtained from MBARI optical instruments on the TAO moorings at 0° 155°W (EP1) and 2°S 170°W (EP2).

The data have been subject to quality control as described in methods. Note: Although EP2 Lwn data from 01-Feb-01

to present is available, data are not included in graph because post-calibration of the above water radiometer is

required.

SeaWiFS Calibration/Validation

Over the last 3 months we have rigorously reprocessed

the full mooring records (1996-2001) for EP1 (0°, 155°W) and

EP2 (2°S, 170°W) to further improve our data quality control

methods. Analysis and improvements to quality control

continue on the reprocessed data. These data have been

uploaded to SeaBass. Figures 5.1 and 5.2 show the time series

of Lu and Lwn, respectively. Figure 5.3 shows the time series

products based on Kd and Lwn. The two methods in Figure 5.3

agree particularly well over a range of chlorophyll

concentrations from ~0.05 to >1 μg l-1. This agreement is

Lwn [

μW cm-2 nm-1 sr-1]
SIMBIOS Project Annual Report

35

further quantified in Figure 5.4a, which directly shows the

comparison of the two methods for EP1.

McClain and Fargion (1999b) showed matchup data

derived from optical profiles of the SPMR in the equatorial

Pacific. These data indicated excellent agreement between the

satellite- and profile-derived water-leaving radiance values.

Similarly, Figure 4b compares the surface chlorophyll values

obtained from SPMR casts with the extracted chlorophyll

samples obtained at the same location.

5.4 FUTURE WORK

Nome recent setbacks related to hardware failures and

vandalism, the two major mooring installations at 0°, 155°W

and 2°S, 170°W are now operating relatively well. The recent

deployment of hyperspectral instruments at both moorings will

better support validation efforts for future ocean color

missions.

This year alone we have conducted over 50 SPMR profile

casts, analyzed more than 2500 shipboard chlorophyll

measurements, and collected close to 500 HPLC samples. In

the near future we intend to analyze these HPLC data in

conjunction with the optical profile data in order to better

understand the pigment distribution in the euphotic zone and

its relationship to water leaving radiance. Further, we are now

in the process of updating our analysis of nutrient samples.

The program of cruise-based measurements as described

above will continue on eight equatorial Pacific cruises during

2002-2003, with scheduled SeaWiFS LAC where applicable,

to enhance the probability of obtaining valid matchups.

REFERENCES

Chavez, F.P., and R.T. Barber, 1987: An estimate of new

production in the equatorial Pacific, Deep-Sea Research,

34, 1229-1243.

Chavez, F.P., K.R. Buck, R.R. Bidigare, D.M. Karl, D. Hebel,

M. Latasa, L. Campbell and J. Newton, 1995: On the

chlorophyll a retention properties of glass-fiber GF/F

filters. Limnology and Oceanography, 40, 428-433.

Chavez, F.P. and J. R. Toggweiler, 1995: in Upwelling in the

Ocean: Modern Processes and Ancient Records C. P.

Summerhayes, K. C. Emeis, M. V. Angel, R. L. Smith,

B. Zeitzschel, Eds. (J. Wiley & Sons, Chichester, 1995)

313-320.

Chavez, F.P., K.R. Buck, S.K. Service, J. Newton and R.T.

Barber 1996: Phytoplankton variability in the central and

eastern tropical Pacific. Deep-Sea Research, 43(4-6), 835-

870.

Chavez, F.P., P.G. Strutton, and M.J. McPhaden, 1998:

Biological-physical coupling in the central equatorial

Pacific during the onset of the 1997-98 El Niño.

Geophysical Research Letters, 25(19), 3543-3546.

Chavez, F.P., P.G. Strutton, G.E. Friederich, R.A. Feely, G.

Feldman, D. Foley and M.J. McPhaden, 2000: M.J.

Biological and chemical response of the equatorial Pacific

Ocean to the 1997-98 El Niño, Science, 286, 2126-2131.

Chavez, F.P., D. Wright, R. Herlien, M. Kelley, F. Shane and

P.G. Strutton, 2000: A device for protecting moored

spectroradiometers from bio-fouling. Journal of Oceanic

and Atmospheric Technology, 17, 215-219.

Dugdale, R.C. and F.P. Wilkerson 1998: Silicate regulation of

new production in the equatorial Pacific upwelling,

Nature, 391, 270-273.

Feely, R.A., R. Wanninkhof, T. Takahashi and P. Tans 1999:

Influence of El Niño on the equatorial Pacific contribution

to atmospheric CO2 accumulation. Nature, 398, 597-601.

Frouin, R., D.W. Ligner, and C. Gautier, 1989: A simple

analytical formula to compute clear sky total and

photosynthetically available solar irradiance at the ocean

surface. Journal of Geophysical Research, 94, 9731-9742.

McClain C.R. and G.S. Fargion, 1999a: SIMBIOS Project

1998 Annual Report. NASA Tech. Memo. 1999-208645,

Eds., NASA Goddard Space Flight Center, Goddard,

Maryland, 105pp.

McClain C.R. and G.S. Fargion, 1999b: SIMBIOS Project

1999 Annual Report. NASA Tech. Memo. 1999-209486,

Eds., NASA Goddard Space Flight Center, Goddard,

Maryland, 137pp.

Morel, A. 1988: Optical modeling of the upper ocean in

relation to its biogenous matter content (Case I waters).

Journal of Geophysical Research, 93, 10749-10768.

Strutton, P.G and Chavez, F.P. Radiant heating in the

equatorial Pacific: Spatial and Temporal variability.

Deep-Sea Research I, in revision.

Ryan, J.P., P.S. Polito, P.G. Strutton and F.P. Chavez,

Anomalous phytoplankton blooms in the equatorial

Pacific. Progress in Oceanography, in revision.

Strutton, P.G., J.P Ryan and F.P. Chavez, 2001: Enhanced

chlorophyll associated with tropical instability waves in

the equatorial Pacific. Geophysical Research Letters,

28(10), 2005-2008.

Strutton, P.G. and F.P. Chavez, 2000: Primary productivity in

the equatorial Pacific during the 1997-98 El Niño.
SIMBIOS Project Annual Report

36

Journal of Geophyiscal Research, 105(C11), 26,089-

26,101.

Strutton, P.G., Chavez, F.P., Ryan, J.P. and Friederich, G.E.,

2002: Merging data from ships, satellites, moorings and

drifters to understand biological-physical coupling in the

oceans. In preparation for ‘Handbook of scaling methods

in aquatic ecology: Measurement, analysis and

simulation’, CRC Press.

Tans, P.P., I. Y. Fung, T. Takahashi 1990: Observational

constraints on the global atmospheric CO2 budget,

Science, 247, 1431-1438.

Takahashi, T, R.A. Feely, R.F. Weiss, R. Wanninkhof, D.W.

Chipman, S.C. Sutherland and T.T. Takahashi 1997:

Carbon dioxide and climate change. Proceedings of the

National Academy of Sciences, 94, 8314-8319.

EP1

0.0

0.3

0.5

0.8

1.0

1.3

1.5

1996 1997 1998 1999 2000 2001

2 0

2 2

2 4

2 6

2 8

3 0

3 2 M ooring C H L

SeaW IFS C H L

Shipboard C H L

SST

EP2

0.0

0.3

0.5

0.8

1.0

1.3

1.5

1996 1997 1998 1999 2000 2001

2 0

2 2

2 4

2 6

2 8

3 0

3 2

M ooring C H L

SeaW IFS C H L

Shipboard C H L

SST

Year

Figure 5.3: The time series of chlorophyll derived from the MBARI optical instruments on the TAO moorings at 0° 155°W

(EP1) and 2°S 170°W (EP2). For comparison, SeaWiFS chlorophyll for the mooring location is also plotted (circles). The time

series of SST indicates that the highest chlorophyll concentrations occurred during the retreat of the 1997-98 El Niño, in mid

1998 at EP1. SeaWiFS and mooring chlorophyll exhibit relatively good agreement over more than an order of magnitude of

chlorophyll concentrations. Mooring/SeaWiFS/Shipboard Chl (

μg L-1)

SST (C)
SIMBIOS Project Annual Report

37

A

y = 0.93x + 0.01

R 2

 = 0.64

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

in situ & SeaWiFS Chl ( g L-1)

M ooring C hl (

μg L -1 )

M ooring C HL

S hipboard C HL

B

y = 0.62x + 0.05

R 2

 = 0.80

0.0

0.1

0.2

0.3

0.4

0.0 0.1 0.2 0.3 0.4

in situ Chl ( g L-1)

SPM R C hl (

μg L -1 )

SPM R CHL

Figure 5.4: (A) Comparison of SeaWiFS and shipboard in situ chlorophyll with mooring-derived chlorophyll for the MBARI

instruments at 0° 155°W (EP1). Mooring-derived chlorophyll is calculated using algorithm by Morel, A. (1988). Regression

analysis for SeaWiFS vs. Mooring Chl is also shown. (B) Comparison of chlorophyll from shipboard measurements with

chlorophyll calculated from SPMR casts. SPMR chlorophyll is calculated using OC4V4 algorithm by O’Reilly, J.O. All data

have been subject to quality control.

Table 5.1: Summary of cruises during which in situ data have been obtained by MBARI in support of SIMBIOS. All cruises

were undertaken aboard the NOAA ship Ka’imimoana, with the exception of GasEx II cruise aboard the Ronald H. Brown

(see: www.pmel.noaa.gov/co2/gasex2). Meridional transects indicate the lines occupied by the ship. Along each line, CTD

stations were performed approximately every degree of latitude from 8°N to 8°S, except for GasExII. Measurements consisted

of extracted chlorophyll (Chl) plus nitrate, phosphate and silicate (Nutrients) at 8 depths between 0 and 200m. On selected

cruises, primary production (PP) and new production (NP) measurements were also made using 14C and 15N incubation

techniques, respectively. Daily optical profiles were obtained using the Satlantic SeaWiFS Profiling Multispectral Radiometer

(SPMR) where indicated.

Cruise ID Dates Meridional transects Measurements

GP6-00-KA 14-Oct-00 to 14-Nov-00 155W and 170W

Chl, NP, PP, Nutrients,

SPMR

GP7-01-RB 16-Oct-00 to 22-Nov-00 95W and110W Chl, Nutrients

GP8-00-KA 16-Nov-00 to 14-Dec-00 165W and 180W Chl, Nutrients

GP1-01-KA 14-Jan-01 to 15-Feb-01 140W and 125W Chl, Nutrients

GasEx II 28-Jan-01 to 08-Mar-01 125W and 140W

Chl, NP, PP, Nutrients,

SPMR

GP2-01-KA 28-Mar-01 to 05-May-01 95W and 110W Chl, Nutrients

GP3-01-KA 29-May-01 to 29-Jun-01 155W and 170W

Chl, NP, PP, Nutrients,

SPMR

GP4-01-KA 02-Jul-01 to 31-Jul-01 165E and 180 Chl, Nutrients

GP5-01-KA 13-Aug-01 to 03-Sep-01 125W and 140W Chl, Nutrients

GP7-01-KA 28-Sep-01 to 25-Oct-01 155W and 170W

Chl, NP, PP, Nutrients,

SPMR

GP8-01-KA 01-Nov-01 to Dec-01 165E and 180 Chl, Nutrients
SIMBIOS Project Annual Report

38

This research was supported by the

SIMBIOS NASA contract # 00193

PEER REVIEWED PUBLICATIONS

Chavez, F.P., Strutton, P.G., Friederich, G.E., Feely, R.A.,

Feldman, G.C., Foley, D.G. and McPhaden, M.J., 1999:

Biological and chemical response of the equatorial Pacific

Ocean to the 1997-98 El Niño, Science. 286, 2126-2131.

Chavez, F.P., Strutton, P. G. & McPhaden, M. J., 1998:

Biological-physical coupling in the equatorial Pacific

during the onset of the 1997-98 El Niño. Geophysical

Research Letters., 25(19), 3543-3546.

Chavez, F.P., Wright, D., Herlien, R., Kelley, M., Shane, F.

and Strutton, P.G., 2000: A device for protecting moored

spectroradiometers from bio-fouling. Journal of Oceanic

and Atmospheric Technology, 17, 215-219.

Schwarz, J.N., Gege, P., Kowalczuk, P., Kaczmarek, S.,

Carder, K.L., Muller-Karger, F., Varela,R., Chavez, F.P.,

Clark, D.K., Cota, G.F., Cunningham, A., McKee, D.,

Kishino, M., Mitchell, B.G., Phinney, D.A., Raine, R.,

Trees, C.C., Zaneveld, J.R.V., Pegau, S. Two models for

Gelbstoff absorption. Oceanologia, in preparation..

Strutton, P.G and Chavez, F.P. Radiant heating in the

equatorial Pacific: Spatial and Temporal variability.

Deep-Sea Research I, in revision.

Ryan, J.P., Polito, P.S., Strutton, P.G. and Chavez, F.P.

Anomalous phytoplankton blooms in the equatorial

Pacific. Progress in Oceanography, in revision.

Strutton, P.G., Ryan, J.P. and Chavez, F.P. 2001: Enhanced

chlorophyll associated with tropical instability waves in

the equatorial Pacific. Geophysical Research Letters,

28(10), 2005-2008.

Strutton, P.G. and Chavez, F.P. 2000: Primary productivity in

the equatorial Pacific during the 1997-98 El Niño.

Journal of Geophyiscal Research, 105(C11), 26,089-

26,101.

OTHER PUBLICATIONS

O'Reilly, J. E., Maritorena, S., O’Brien, M.C., Siegel, D.A.,

Toole, D., Menzies, D., Smith, R.C., Mueller, J.L.,

Mitchell, B.G., Kahru, M., Chavez, F.P., Strutton, P.G.,

Cota, G.F., Hooker, S.B., McClain, C.R., Carder, K.L.,

Muller-Karger, F., Harding, L., Magnuson, A., Phinney,

D., Moore, G.F., Aiken, J., Arrigo, K.R., Letelier, R.M.

and Culver, M. SeaWiFS Postlaunch Calibration and

Validation Analyses, Part 3, SeaWiFS Postlaunch

Technical Report Series, Volume 11, NASA Tech.

Memo. 2000-206892, S.B. Hooker and E.R. Firestone,

Eds., NASA Goddard Space Flight Center, Greenbelt,

Maryland, 2001. .

PRESENTATIONS

Chavez, F.P., Feldman, G.C., McPhaden, M.J., Foley, D.G.

and Strutton, P.G., 1998: Remote Sensing of the

Equatorial Pacific during 1997-98. AGU Fall Meeting,

San Francisco, CA, December, Invited

Friederich, G.E., Chavez, F.P., Strutton, P.G. and Walz, P.,

1998: Bio-optical and Carbon Dioxide Time Series From

Moorings in the Equatorial Pacific During 1997-1998.

AGU Fall Meeting, San Francisco, CA.

Friederich, G E Walz, P M Strutton, P G Burczynski, M G

Chavez, F P Seasurface pCO2 Time Series from Moorings

in Equatorial and Coastal Upwelling Systems. AGU

Ocean Sciences meeting, San Antonio, TX.

Kuwahara, V.K., Strutton, P.G., Penta, B., Drake, E., and

Chavez, F.P., 2001: Bio-optics from Moorings: Satellite

Validation and Ecosystem Dynamics. AGU Fall Meeting,

San Francisco, CA, December.

Strutton, P.G., Merging data from ships, satellites and

moorings to understand biological-physical coupling in

the equatorial Pacific, Invited seminar, Oregon State

University, November 2001.

Strutton, P.G. and Chavez, F.P., Temporal and spatial

variability of phytoplankton productivity during GasExII,

Oral presentation, GasExII workshop, Seattle, October

2001.

Strutton, P.G., Chavez, F.P., Drake, E., Kuwahara, V.K,

Friederich, G. and Ryan, J., 2001: Merging Data from

Ships, Satellites and Moorings to Understand BiologicalPhysical Coupling in the Equatorial Pacific. AGU Fall

Meeting, San Francisco, CA, December.
SIMBIOS Project Annual Report

39

Strutton, P.G., Chavez, F.P., Foley, D.G., Sclining, B.M. and

McPhaden, M.J., 1999: Remote Sensing of BiologicalPhysical Coupling in the Equatorial Pacific. ASLO Ocean

Sciences Meeting, Santa Fe, NM.

Strutton, P.G., Chavez, F.P. and McPhaden, M.J., 1998: In situ

measurements of primary productivity in the equatorial

Pacific during the 1997-98 El Niño. AGU Fall Meeting,

San Francisco, CA.

Strutton, P.G., Chavez, F.P. and McPhaden, M.J., 1998:

Primary productivity in the equatorial Pacific during the

onset of the 1997-98 El Nino. AGU/ASLO Ocean

Sciences Meeting, San Diego, CA.

Strutton, P.G., Chavez, F.P. and McPhaden, M.J., 1998:

Primary productivity in the equatorial Pacific during the

onset of the 1997-98 El Nino. Pacific Climate Workshop,

Catalina Island, CA.

Strutton, P.G., Ryan, J.P., Polito, P. and Chavez, F.P., 2000:

High Chlorophyll in the Equatorial Pacific During 1998

Associated With Tropical Instability Wave Fronts. AGU

Ocean Sciences meeting, San Antonio, TX

Strutton, P.G., Chavez, F.P. and McPhaden, M.J., 1998:

Biological-physical coupling in the central equatorial

Pacific during the 1997-98 El Niño, Ocean Optics XIV,

Kailua-Kona, H
SIMBIOS Project Annual Report

40

Chapter 6

Satellite Ocean-Color Validation Using Ships of Opportunity

Robert Frouin, David L. Cutchin

Scripps Institution of Oceanography, University of California San Diego, La Jolla

Pierre-Yves Deschamps

Laboratoire d’Optique Atmosphérique, Université des Sciences et Technologie de Lille, France

6.1 INTRODUCTION

Our investigation’s objective is to continue and extend a

ship-of-opportunity program (merchant ships, research

vessels) developed during the last four years to collect

concomitant normalized water-leaving radiance and aerosol

optical thickness over the world’s oceans. A global, long-term

data set of these variables is needed to verify whether satellite

retrievals of normalized water-leaving radiance are within

acceptable error limits and, eventually, adjust atmospheric

correction schemes. The volunteer ships are equipped with

portable SIMBAD radiometers that measure both variables in

typical spectral bands of satellite ocean color sensors.

The basic measurement program includes research cruises

and merchant ship voyages mostly in the Pacific Ocean (SIO

network); it complements the ships/routes operated by the

University of Lille in the Atlantic and Indian Oceans (LOA

network). The data collected, after proper quality control and

processing, are delivered to the SIMBIOS project office

(SeaBASS archive). They complement in situ data acquired by

other satellite validation programs, including the mostly landbased AERONET aerosol-monitoring network.

The SIMBAD data collected by the SIO and LOA

networks are intended to check the radiometric calibration of

satellite sensors after launch and to evaluate satellite-derived

ocean color variables (i.e., normalized water-leaving radiance,

aerosol optical thickness, aerosol type). Analysis of the

SIMBAD data is expected to provide information on the

accuracy of satellite retrievals of water-leaving radiance, an

understanding of the discrepancies between satellite and in

situ data, and algorithms that reduce the discrepancies,

contributing to more accurate and consistent global ocean

color data sets.

6.2 RESEARCH ACTIVITIES

During the period from 01 November 2000 to 31 October

2001, SIMBAD measurements were made during 12 cruises of

opportunity, including one merchant ship voyage. The cruises

are listed below with name of cruise, SIMBAD instrument

used, region of measurements, name of operator, and dates of

measurements. The location of the measurements is displayed

in Fig. 6.1. The data were collected mostly off the West Coast

of the United States (CalCOFI cruises) and in the Sea of Japan

(ACE-Asia experiment). 1540 complete data sets were

produced, bringing to 4054 the total number of data sets

available since October 1996 (i.e., in the SeaBASS archive).

Excluding the three ongoing campaigns (IOFFE01,

PERTH0102, and CalCOFI 0110NH –see Table 6.1), a total of

51 campaigns with SIMBAD measurements were realized

during October 1996-Otober 2001.

 Table 6.1: SIMBAD cruises during 11/1/2000 – 12/31/2001

CalCOFI 0101JD, Simbad03, Southern California Bight, David Starr

Jordan, Antoine Poteau, 07 Jan 01 - 22 Jan 01

AMLR2001, Simbad06, Antarctica, R/V Yuzhmorgeologiya, John

Wieland, 08 Feb 01 - 05 Mar 01

ACEASIA2001, Simbad03/07, Honolulu-Yokosuka, R/V Ron Brown,

Robert Frouin, 26 Feb 01 - 20 Apr 01

IMECOCAL 0106/07, Simbad08, Ensenada-San Carlos, R/V Francisco de

Ulloa, Elsa Alguirre, 26 Jun 01 - 16 July 01

CalCOFI 0107NH, Simbad03, Simbad07, Southern California Bight, R/V

New Horizon, Antoine Poteau, 10 Jul 01 - 27 Jul 01

JUN01KN, Simbad06, Tropical Atlantic, R/V Knorr, Ajit Subranamian, 25

Jun 01 - 19 Aug 01

P380108, Simbad07, Honolulu-Valdez, M/V Chevron Mississipi, David

Cutchin, 04 Aug 01 - 12 Aug 01

PERTH0102, Simbad08, offshore Perth, Australia, Mervyn Lynch, Sep 01

- Sept 02

IOFFE01, Simbad03, Simbad07, R/V Akademik Ioffe, Kiel-RecifeUshuaia- Antarctica (and back), 05 Oct 01 - April 02

CalCOFI 0110NH, Simbad06, Southern California Bight, R/V New

Horizon, Antoine Poteau, 25 Oct 01 - 11 Nov 01
SIMBIOS Project Annual Report

41

To extend the SIMBAD measurement program, last year

several collaborative efforts were initiated. First, CICESE,

Mexico agreed to our participation in the regular, quarterly

IMECOCAL cruises off Baja California. The advantage of the

IMECOCAL cruises, compared with the CalCOFI cruises, is

that they take place in a less cloudy region, providing more

match-ups per cruise. Second, a cooperative agreement was

signed between the Scripps Institution of Oceanography and

the Shirshov Institute of Oceanology to collect ocean-color

evaluation data during the yearly, 6-month R/V Akademik

Ioffe cruises from Kaliningrad, Russia to Antarctica via

Ushuaia, Argentina. The measurement program includes not

only SIMBAD measurements, but also collection of water

samples for pigment and absorption analysis. Third, SIMBAD

measurements now are complementing an ongoing satellite

calibration and evaluation program undertaken by Curtin

University, with cruises every 6 weeks off Perth, Australia

along a 40 km transect perpendicular to the coast.

Version 1.6 of the SIMBAD processing code was

released, with creation of a special PC version of the code

(there are differences in the way Unix and PC Fortran

compilers behave) and creation of PC executable online. Code

can be downloaded at http://polaris/~simbad/Software.html.

The calibration coefficients of the various SIMBAD

instruments for sun and sea modes were analyzed statistically

over the period October 1996-October 2001. The analysis

revealed a significant loss of instrument sensitivity with time

at 560 nm, especially for instrument No. 8 (a change of 1.5%

in the sun-mode coefficient, equivalent to 0.2 in optical

thickness, during the 4-year period). However, with a suitable

temporal fit the variability is reduced. Examination of the

history of calibration coefficients since October 1996 indicates

that, due to uncertainty in the calibration coefficients,

measurements of optical thickness are made with an accuracy

of ±0.02 and measurements of water-leaving radiance with a

relative accuracy of ±3% (Fig. 6.2).

6.3 RESEARCH RESULTS

A long-term, monthly climatology of near-surface

phytoplankton pigment concentration was also built by

integrating monthly data sets from various satellite sensors,

namely CZCS, POLDER, and SeaWiFS. The grid used is an

equal-angle, rectilinear grid of size 2048 lines and 4096 pixels,

for the full global range. This is the standard SeaWiFS grid

used for the L3 monthly products. Data periods were Nov 78 -

Jun 86 for CZCS, Nov 96 - Jun 97 for the POLDER

instrument, and Sep 97 - Nov 00 (at this time) for SeaWiFS.

The processing basics are described below.

• The SeaWiFS pigment data were extracted from each of

the 40 monthly global SeaWiFS files and then 12

averages were formed, one for each month of the year.

• The CZCS pigment data were extracted from each of the

90 monthly global CZCS files, and 12 averages were

formed, as above.

• The POLDER pigment data was extracted from each of

the 8 monthly global POLDER files, missing values were

filled in (to a depth of 5 layers), and an interpolation made

to the smaller SeaWiFS grid.

• The CZCS monthly averages were expanded out to the

larger SeaWiFS grid, and then composite monthlies were

formed by reconstituting the sums from the three data sets

(since the number of pixels averaged were saved for each)

and forming monthly super-averages.

• The monthly composites were read in, and the number of

good time-series data samples per pixel was determined

(including the use of the land and sea-ice masks). If at

least 2 good samples were found for a given pixel, a linear

time-interpolation was made for that pixel, and the

estimated values were used to fill whatever missing

values were present.

• A Delaunay triangulation of available nearest neighbors

was finally applied to compute linear interpolates that

replaced missing values in the grid.

Figure 6.3 displays the resulting pigment concentration

field for January and July, and Figure 6.4 the number of points

used in the averages. The missing data at high latitudes during

winter are filled adequately. A comparison of the SeaWiFS

and CZCS monthly data sets gives a correlation coefficient of

0.59, a bias of 0.86 mgm-3 (higher SeaWiFS values), and a

root-mean-squared difference of 0.13 mgm-3. The higher

SeaWiFS values during Sep 97 - Nov 00 may be attributed to

higher concentrations in the tropical Pacific after the 1997-

1998 El Niño, although algorithm errors (e.g., due to

atmospheric correction) are not excluded. In the next step

OCTS and in situ pigment concentration data from the

SeaBASS archive (period 1961-2000) will be integrated, after

calibration of the various satellite data sets (taken separately)

against the in-situ data. A new algorithm to estimate

phytoplankton chlorophyll concentration from SeaWiFS topof-atmosphere radiance was developed. The algorithm utilizes

the change in local spatial contrast of the SeaWiFS radiance at

443 and 555 nm. Assuming that the atmosphere does not

change over short distances (a few kilometers), the ratio of the

spatial contrast in the blue and green is a monotonous function

of the pigment concentration. The effect of a changing

atmosphere over a few kilometers is taken into account, at

least partly, by subtracting the 865 nm radiance from the blue

and green radiance. Preliminary results show that the ratio of

spatial contrasts in the blue and green is not noisy, and is

strongly correlated to the corresponding chlorophyll

concentration (as derived by the standard SeaWiFS project

bio-optical algorithm) (Fig. 6.5). The advantage of the new

algorithm is that no explicit atmospheric
SIMBIOS Project Annual Report

42

Figure 6.1: Location of the SIMBAD data sets
SIMBIOS Project Annual Report

43

0.01

0.02

0.03

0.04

0.05

0.06

0.07

400 500 600 700 800 900

Sun calibration coefficient

Simbad 03

Simbad 06

Simbad 07

Simbad 08

Wavelength (nm)

a

0

1

2

3

4

5

400 500 600 700 800 900

Sea calibration coefficient

Simbad 03

Simbad 06

Simbad 07

Simbad 08

Wavelength (nm)

b

Figure 6.2: Standard deviation of sun and sea calibration coefficients for various Simbad instruments during the

period October 1996-October 2001, trend removed (see text).
SIMBIOS Project Annual Report

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Figure 6.3: January and July distribution of near-surface phytoplankton pigment concentration in the world’s oceans from

CZCS, POLDER, and SeaWiFS data.

Figure 6.4: Number of observations used in the averages of Figure 6.3.
SIMBIOS Project Annual Report

45

Figure 6.5: Imagery of chlorophyll concentration derived with the standard SeaWiFS algorithm (left) and corresponding

imagery of the ratio of spatial contrast in SeaWiFS radiance at 555 nm and 443 nm (right). The data were acquired off the coast

of South Africa on 14 February 1999. Note the strong correlation between the two types of images.

correction of the top-of-atmosphere radiance is required, but

more tests are indeed necessary before concluding about the

general applicability of the algorithm.

The ACE-Asia experiment provided a comprehensive

calibration and validation data set covering a wide range of

oceanic and atmospheric conditions, with detailed information

on aerosol optical properties, type, and vertical distribution.

Figure 6 displays a time series of SIMBAD measurements of

aerosol optical thickness at 870 nm (Fig. 6a) and Angström

coefficient (Fig. 6b). Aerosol optical thickness was quite low

across the Pacific Ocean from Hawai to Japan (Julian days 75

to 90), with values generally less than 0.2. Close to Japan, in

the Korea Strait, the Sea of Japan, and the East China Sea

(Julian days 91 to 107) aerosol conditions were more variable

with several dust and pollution events. On Julian day 99

optical thickness was especially large (> 0.9 at 870 nm) with a

relatively small Angström coefficient (about 0.3) characteristic

of large particles. At some locations during some days diurnal

variability was pronounced. On Julian day 102 in the Korea

Strait, for example, aerosol optical thickness decreased from

about 0.45 to 0.25 at 870 nm during the period of a few hours,

while Angström coefficient increased from 0.2 to 0.7. The

higher Angström coefficient in the afternoon was associated

with smaller particles, which is consistent with the size

distributions measured onboard the ship.

Figure 6.7 displays the corresponding time series of

SIMBAD-derived marine reflectance (Fig. 6.7a) and

reflectance ratio (Fig. 6.7b). Marine reflectance in the blue

(443 nm and 490 nm) was high during the first part of the

cruise (open ocean waters), until Julian day 90. Then it

remained low yet variable (East coast of Japan, in the Korea

Strait, the Sea of Japan, and the East China Sea). On Julian

day 99 the ship was in the Japan Sea, and the lowest

reflectance at 443 nm (< 0.005) was measured. The reflectance

ratios, 443/560 and 490/560, were low during Julian days 98-

103 when the ship was in the Sea of Japan and in the Korea

Strait, indicating high chlorophyll concentrations during those

days.

A number of good days for evaluating SeaWiFS- and

MODIS-derived ocean-color, with varied and contrasted

conditions, were identified. They are listed below with their

main characteristics.

07 April 01: Korea Strait, weak winds from the Northeast,

calm sea, shallow boundary layer, aerosol optical

thickness of about 0.2 at 870 nm.
SIMBIOS Project Annual Report

46

08 April 01: Sea of Japan, weak wind from the Southwest,

calm sea small particles, aerosol optical thickness of

about 0.2 at 870 nm.

09 April 01: Sea of Japan, weak wind from the East, very calm

sea, dust, aerosol optical thickness of 0.6-0.8 at 870 nm,

high chlorophyll concentration.

12 April 01: Korea Strait, strong wind from the West, thick

boundary layer, whitecaps, dust and pollution, changing

aerosols, multiple aerosol layers, aerosol optical

thickness of 0.3-0.4 at 870 nm.

13 April 01: Korean Strait, strong wind from the West,

whitecaps, aerosols in the boundary layer, pollution,

aerosol optical thickness of about 0.15, North-South

transect toward Coast of Japan.

15 April 01: East China Sea, weak wind, calm sea, transect

Fukua Island – Cheju Island, aerosol optical thickness

at 870 nm increasing toward Cheju (0.07 to 0.16), but

smaller particles.

19 April 01: Pacific Ocean, strong wind from the West,

whitecaps, pollution, aerosol optical thickness of about

0.15 at 870 nm.

6.4 FUTURE WORK

During the second year of the project (01 November

2001-31 October 2002), collection of SIMBAD data will

continue during the quarterly CalCOFI and ICOMECAL

cruises. A more comprehensive measurement program

onboard R/V Ioffe from Kiel to Antarctica and back will be

organized, with eventually a leg from Miami to Kiel dedicated

to the training of students. SIMBAD measurements will also

be made during the regular transects off Perth, Australia. The

focus, however, will be on the following specific tasks.

Five advanced SIMBAD radiometers (SIMBADAs) will

be assembled to complement the four SIMBAD instruments of

the SIO network. The advanced version is lighter and more

compact, operates without cables, has improved electronics,

reduced noise, a larger internal memory, better batteries with

internal charger, easier display of the viewing angles, and

measures in 11 spectral bands instead of 5, from 350 to 870

nm. The necessary software to process the SIMBADA data,

including correction of residual polarization, will be

developed.

The SeaWiFS- and MODIS-derived ocean-color will be

evaluated using in situ data collected during selected days of

ACE-Asia (see above). Even though the number of match-ups

is small, the data set is comprehensive, allowing a detailed and

complete analysis of each case.

A long-term, monthly climatology of phytoplankton

pigment concentration will be created by merging/integrating

in situ data (SeaBASS archive) and satellite data (CZCS,

OCTS, POLDER, and SeaWiFS). This climatology, which can

easily be updated as new data become available, will serve as

a reference in studies of inter-annual variability and will be

useful in various investigations of climate and climate change.

The new algorithm to estimate chlorophyll concentration

from space, based on spatial contrast of the satellite imagery,

will be further investigated. The computation of the spatial

contrast will be refined. Various contrast ratios will be

examined and the relation between these ratios and pigment

concentration, its sensitivity to phytoplankton type, will be

established. The algorithm will be applied to imagery acquired

in varied atmospheric and oceanic regimes and compared with

standard algorithms.

Aerosol models for ocean color remote sensing will be revisited. A classification of aerosol models obtained from

atmospheric optics measurements made by CIMEL

radiometers (AERONET, SIMBIOS) will be made using

Kohonen maps. These models will be compared with those

used in current atmospheric correction schemes and used

alternatively in the schemes. Kohonen mapping will also be

used to classify top-of-atmosphere imagery into aerosol and

water classes that will be labeled (e.g., type of aerosol, range

of pigment concentration, type of water).

ACKNOWLEDGMENTS

We wish to thank all the officers and scientists that have

collected SIMBAD data, Antoine Poteau for processing

SIMBAD data and maintaining the SIMBAD radiometers,

John McPherson for programming support, and the SIMBIOS

project scientists and staff for providing match-up data and for

many helpful discussions (and more). This work is supported

by the National Aeronautics and Space Administration under

contract NAS5-00194, the Scripps Institution of

Oceanography, the Centre National d’Etudes Spatiales, and

the Centre National de la Recherche Scientifique.
SIMBIOS Project Annual Report

47

0

0.2

0.4

0.6

0.8

1

70 75 80 85 90 95 100 105 110

Julian day (2001)

a

0

0.5

1

1.5

70 75 80 85 90 95 100 105 110

Julian day (2001)

b

Figure 6.6: Time series of aerosol optical thickness at 870 nm (a) and Angström coefficient (b) during ACEAsia.
SIMBIOS Project Annual Report

48

-0.005

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

70 75 80 85 90 95 100 105 110

443 nm

490 nm

560 nm

670 nm

Julian day (2001)

a

0

2

4

6

8

10

70 75 80 85 90 95 100 105 110

443nm/560nm

490nm/560nm

Julian day (2001)

b

Figure 6.7: Time series of marine reflectance just above the surface (a) and reflectance ratio (b) during ACEAsia.
SIMBIOS Project Annual Report

49

This research was supported by the

 SIMBIOS NASA contract # 00194

PEER REVIEWED PUBLICATIONS

Nakamoto, S., S. Prasanna Kumar, J. M. Oberhuber, J.

Ishizaka, K. Muneyama, and R. Frouin, 2001: Response

of the equatorial pacific of chlorophyll pigments in a

mixed layer-isopycnal ocean general circulation model.

Geophys. Res. Lett., 28, 2021-2024.

Shell, K., R. Frouin, S. F. Iacobellis, and R. C. J. Somerville,

2001: Influence of Phytoplankton on Climate. Proc. of the

12th Symposium on Global Change and Climate

Variations, January 2001, Albuquerque, New Mexico,

247-250, published by the American Meteorological

Society.

Behrenfeld, M. J., J. T. Randerson, C. R. McClain, G. C.

Feldman, S. O. Los, C. J. Tucker, P. G. Falkowski, C. B.

Field, R. Frouin, W. E. Esaias, D. K. Kolber, and N. H.

Pollack 2001: Biospheric primary production during and

ENSO transition. Science, 291, 2594-2597.

Frouin, R., B. Franz, and M. Wang, 2001: Algorithm to

estimate PAR from SeaWiFS data. Version 1.2–

Documentation.http://orca.gsfc.nasa.gov/seawifs/par/doc/

seawifs_par_wfigs.pdf.

Frouin, R., S. F. Iacobellis, and P.-Y. Deschamps, 2001:

Influence of oceanic whitecaps on the global radiation

budget. Geophys. Res. Lett., 28, 1523-1526.

Holben, D. N., D. Tanré, A. Smirnov, T. F. Eck, I. Slutsker, N.

Abuhassan, W. W. Newcomb, J. S. Schafer, B. Chatenet,

F. Lavenu, Y. F. Kaufman, J. Vande Castle, A. Setzer, B.

Markham, D. Clark, R. Frouin, R. Halthore, A. Karneli,

N. T. O’neill, C. Pietras, R. T. Pinker, K. Voss, and G.

Zibordi, 2001: An emerging ground-based aerosol

climatology: Aerosol optical depth from AERONET. J.

Geophys. Res., 106, 12067-12097.

Tratt, D. M., R. Frouin, and D. L. Westphal, 2001: The April

1998 Asian Dust event: A Southern California

perspective. J. Geophys. Res., 106, 18371-18379.

Nicolas, J.-M., P.-Y. Deschamps, and R. Frouin, 2001:

Spectral reflectance of oceanic whitecaps in the visible

and near-infrared: Aircraft measurements over open

ocean. Geophys. Res. Lett., in press.

Husar, R. B., D. Tratt, D. Jaffe, S. Gasso, T. Gill, N. S.

Laulinen, F. Fu, M. Reheis, Y. Chun, D. Westphal, B. N.

Holben, G. Geymard, I. McKndry, N. Kuring, G. C.

Feldman, C. R. McClain, R. Frouin, J. Merrill, D. Dubois,

F. Vignola, T. Murayama, N. Sugimoto, S. Nickovic, W.

E. Wilson, K. Sassen, B. A. Schichtel, and S. R. Falke,

2001: The Asian Dust event of April 1998. J. Geophys.

Res.,in press.

Voss, K. J., E. J. Welton, P. K. Quinn, R. Frouin, M. Miller,

and R. M. Reynolds, 2001: Aerosol optical depth

measurements during the Aerosols-99 experiment. J.

Geophys. Res., in press.

Nakamoto, S., S. Prasanna Kumar, J. M. Oberhuber, H Saito,

K. Muneyama, and R. Frouin, 2001: Cholorophyll

modulation in mixed layer dynamics in a mixed-layer

isopycnal general circulation model. Proc. Indian Acad.

Sci., in press.

Nakamoto, S., H. Saito, K. Muneyama, T. Sato, S. Prasanna

Kumar, and R. Frouin, 2001. Ocean bio-geophysical

modeling using mixed-layer-isopycal general circulation

model. Recent Developments in Geophysics, in press.

Frouin, R., and S. F. Iacobellis, 2001: Influence of

phytoplankton on the global radiation budget. J. Geophys.

Res., in revision.
SIMBIOS Project Annual Report

50

Chapter 7

Merging Ocean Color Data From Multiple Missions

Watson W. Gregg

NASA Goddard Space Flight Center, Greenbelt, Maryland

7.1 INTRODUCTION

 We propose to investigate, develop, and test algorithms

for merging ocean color data from multiple missions. We seek

general algorithms that are applicable to any retrieved Level-3

(derived geophysical products mapped to an Earth grid) ocean

color data products, and that maximize the amount of

information available in the combination of data from multiple

missions. Most importantly, we will investigate merging

methods that produce the most complete coverage in the

smallest amount of time, nominally, global daily coverage.

We will emphasize 4 primary methods: 1) averaging, 2)

subjective analysis, 3) blending, and 4) statistical (optimal)

interpolation. We will also assess the ability to produce fuller

coverage in larger time increments, including 4-day, 8-day

(weekly), monthly, and seasonal. Secondarily, we will

investigate the ability of the missions to produce coverage at

different times of day, for diel variability and dynamical

evaluations, and develop algorithms to produce this

information, again on as full a spatial coverage as possible.

We intend to develop methods that are not mission-specific,

but take advantage of the unique characteristics of the

missions as much as possible. However, given the

peculiarities of sensor design and performance, and mission

characteristics, we acknowledge that individual merging

methods may be required to take full advantage of the unique

characteristics of the missions as much as possible, and

produce the highest quality data set.

7.2 RESEARCH ACTIVITIES

Work has focused on analysis, development, and testing

of candidate merger algorithms. We began investigating four

possible approaches for merging ocean color satellite data: (1)

simple splicing/averaging, where data from two or more

satellites are averaged where they coexist at grid points, and

use of a single satellite in gaps where only one exists; (2)

subjective analysis, where specific dependences and

deficiencies are identified using knowledge about sensor

environmental conditions and co-located observations are

merged using different weighting functions for the sensors;

(3) the Conditional Relaxation Analysis Method (CRAM),

where the best data are selected as interior boundary

conditions into a merged set using Poisson's equation; and (4)

optimal interpolation, where merging occurs by weighting

individual sensor data to minimize spatial covariance function.

The latter two algorithms were developed under separate

funding. The algorithms have been characterized and

implemented in software; their capabilities and limitations are

known, as well as their ability to be modified. The application

of algorithms and potential modifications depends on a

characterization of the merged data sets.

Significant progress has been made on each of these

techniques with the majority of analysis this year focusing on

blending. The blended analysis has traditionally been applied

to merging satellite and in situ data. Also known as the

Conditional Relaxation Analysis Method (CRAM), this

analysis assumes that in situ data are valid and uses these data

directly in the final product. To blend multiple satellite ocean

color data sets, one sensor's data would replace the role of the

in situ data as the internal boundary condition (IBC). In

January 2001, initial results of the application of blended

analysis using SeaWiFS and early MODIS data were

presented at the SIMBIOS Science Team Meeting in

Greenbelt, Maryland. SeaWiFS chlorophyll data were used as

the IBC. The radiometric calibration of the MODIS data was

insufficient for scientific use but served the purpose of

algorithm investigation. For this preliminary analysis, six

weeks of daily Level-3 globally mapped MODIS and

SeaWiFS data were obtained from the Goddard DAAC.

Routines were written in IDL (Interactive Data Language) to

extract the MODIS and SeaWiFS chlorophyll images from

their native HDF (Hierarchical Data Format), to scale and

orient the images properly, to retrieve the accompanying

quality flags for MODIS, and to save the resulting images as

FORTRAN binary. Global equal-area mapped MODIS data is

available at 4-km, 36-km, and 1-degree; SeaWiFS standard

mapped images are at 9-km resolution. Both are available

daily, weekly, monthly and yearly. For testing, the blend code

was modified to run at different spatial resolutions and several

weeks of blended results were obtained using daily 36-km and

1-degree data. Running the blend code at a higher spatial

resolution on a global data set is possible, but time consuming.
SIMBIOS Project Annual Report

51

Generally, MODIS pixels extend further north and south into

the upper latitudes than SeaWiFS. Because SeaWiFS is the

IBC and is not present in this area, the blending in these

regions is suspect. Therefore, the blend code was modified to

identify the latitudinal extent of the input SeaWiFS data and to

restrict blending in these high latitudes to avoid incorrect

results. While blending significantly reduces sensor gaps, even

more success can be achieved by pre-averaging data in time.

Therefore, 2- and 4-day averages of 1-degree SeaWiFS and

MODIS data were created and used as inputs into the blend

analysis. Statistical analyses were undertaken to determine the

relative contribution of SeaWiFS to the blended product. In

addition, various techniques were tested to determine how

well the blended analysis was working. For example, a

simulated SeaWiFS image (a 4-day 1-degree data set with

every other value set to 0.0 in checkerboard fashion) was

blended with MODIS. Differences between withheld

SeaWiFS pixels and blended pixels were then compared.

Using averaged 4-day input MODIS and SeaWiFS data, a

proof-of-concept comparison was made between blended

output and results using another ocean color merging

algorithm, averaging. These preliminary results suggested that

the blending analysis can significantly enhance ocean color

data sets, but that improvements in MODIS quality were

necessary before the assessment of merging algorithms could

reliably be determined in a quantitative fashion. Throughout

the year, close contact was kept with MODIS personnel on

data quality, production, and availability issues, as MODIS

data quality drove the schedule. Finally, beginning in midAugust 2001, calibrated MODIS data were made available.

According to MODIS personnel, the Ocean Version 3.2.x

(a.k.a. Collection 3) data products show dramatic

improvements over the prior Version 1 as this new version

reflects improved science algorithms, image de-striping, and

band-band gain adjustments. Therefore, we have begun to

order, download and reformat each of MODIS's three

chlorophyll products: chlor_MODIS, chlor_a_2, and

chlor_a_3 along with their associated quality control files.

Using the quality flag files for each product, data deemed to

be bad, fair or questionable is omitted. As of this writing

(10/15/01), 30 days of re-calibrated MODIS data have been

obtained from the Goddard DAAC and reformatted. It should

be noted that the DAAC's current interface for ordering

MODIS data is cumbersome because each data product for

each day must be specified. Ordering a range of days is not

permitted. To determine which of the three MODIS

chlorophyll products is most compatible with SeaWiFS, we

researched each MODIS algorithm and performed our own

statistical intercomparisons. Using daily SeaWiFS data

regridded to 36-km from its original 9-km resolution and

'good' quality 36-km MODIS data, regional and global root

mean squares (rms) revealed that the 'chlor_a_2' product best

matches SeaWiFS chlorophyll at these spatial and temporal

resolutions (62% rms). This is not surprising as this product is

analogous to the SeaWiFS retrieval algorithm. The

‘chlor_a_3’ algorithm (semi-analytic) had an rms difference of

105% and the ‘chlor_MODIS’ was 154%. SeaWiFS data

characterization is ongoing. An analysis to examine the

relationship between the Level-3 Standard Mapped Images

(SMI) and the binned data sets was undertaken to determine

which data are best suited for blending. The binned values

were formatted to be comparable with the SMI using IDL and

FORTRAN programs. Statistics and histograms revealed that

either data set would be useful for blending, and since the SMI

product is already mapped, it will be used in future blending.

An investigation was made to examine a possible source of

reduced quality SeaWiFS data related to absorbing aerosols.

If such reduced quality were identified using aerosol data then

it could possibly be controlled for, thereby producing a higher

quality merged ocean color data product. Therefore, several

days of Total Ozone Mapping Spectrometer (TOMS) aerosol

index data were downloaded and converted into FORTRAN

binary images. Using IDL, scatterplots comparing daily

SeaWiFS chlorophyll and TOMS Earth Probe aerosol index

were prepared but showed no relationship. This suggests that

the SeaWiFS aerosol masking algorithms are working

properly by correctly identifying absorbing aerosols or that the

TOMS index is inadequate for characterization. Several

preliminary tasks (such as creating a new land-ocean mask,

determining the optimum number of blend iterations,

intercomparing the MODIS chlorophyll products, and

updating statistical analysis code) were completed and now

blending of daily re-calibrated MODIS (specifically, 'good'

quality 'chlor_a_2') and SeaWiFS at 36km resolution has

begun. Initial results suggest that modifications may be

necessary to the algorithm based on incompatibilities of the

data sets. A log transformation is under consideration.

7.3 FUTURE WORK

An invitation to join the International Ocean Colour

Coordinating Group's (IOCCG) working group on data

binning issues was accepted and a first draft of a section on

data assimilation requirements for ocean color data products

was completed. Attendance at a workshop in France in

October was accepted, but had to be cancelled due to travel

restrictions. An offer to contribute to the meeting otherwise

has been tendered to the convener, David Antoine. The

workshop is still on schedule for late October. While working

with 36-km MODIS data in January 2001, a binning problem

was discovered. We prepared statistics and histograms in IDL

to illustrate the problem and contacted the MODIS team. As a

result, the MODIS Level 3 Ocean 36-km and 1-degree data

files were temporarily unavailable to the public until the

problem was corrected.
SIMBIOS Project Annual Report

52

Chapter 8

Bio-Optical and Remote Sensing Observations in Chesapeake Bay

Lawrence W. Harding Jr. and Andrea Magnuson

University of Maryland Center for Environmental Science, Cambridge, Maryland

8.1 INTRODUCTION

Bio-optical measurements in turbid near-shore waters are

essential to recover chlorophyll concentrations (chl-a) from

satellite remote sensing of ocean color because of the complex

optical properties of these waters and the high variability of

constituents that influence light absorption and reflectance.

Radiance-ratio algorithms, such as OC4v.4 that is used in

SeaWiFS standard processing for chl-a, are effective for

oceanic Case 1 waters (Morel and Prieur, 1977; Gordon and

Morel, 1983) where there is a co-varying relationship of

remote-sensing reflectance (RRS) and chl-a (e.g. O’Reilly et

al., 1998, 2000). In contrast, freshwater inputs to Case 2

coastal waters are accompanied by high concentrations of

suspended material and colored dissolved organic matter

(CDOM) that may dominate absorption and vary

independently of chl-a. Bio-optical algorithms for Case 2

waters must account for the absorption and scattering of

multiple components of the water column. Coastal waters with

high particle scattering pose additional problems with respect

to atmospheric correction. SeaWiFS atmospheric correction

procedures assume water-leaving radiances are negligible in

the NIR, which can be an invalid assumption in Case 2 waters

(Siegel et al., 2000). In such cases, part of the satellite signal

that has its source in the water column is inappropriately

attributed to the atmosphere. LWN returned by standard

SeaWiFS processing can be underestimated and sometimes

negative, resulting in overestimates of chl-a when an empirical

radiance ratio algorithm, such as OC4v.4, is used (Bailey et

al., 2000).

A significant part of our SIMBIOS effort has been to

provide ship-based bio-optical measurements for Chesapeake

Bay and adjacent coastal waters of the Mid-Atlantic Bight

(MAB) to aid in algorithm development and validation of

satellite-derived products for Case 2 waters. Chesapeake Bay

is an important Case 2 validation site because of the extensive

data set of pigment, bio-optical and related in situ

measurements that is available from past and on-going

research efforts. Since 1995, a series of three cruises per year

(spring, summer and fall) has been conducted, providing a

time-series of bio-optical measurements with seasonal

coverage. In collaboration with other research groups, we have

continued this cruise series in 2001, and will continue through

2003. The bio-optical data set now consists of approximately

400 stations with measurements of both inherent (dissolved,

pigmented particulate and non-pigmented particulate

absorption spectra) and apparent (downwelling

radiance/upwelling irradiance) optical profiles, over 600

stations with pigment analyses by high performance liquid

chromatography (HPLC), and over 2,100 measurements of

fluorometric chl-a. All of these data have been submitted to

SeaBASS. We have used these data to validate SeaWiFS

products for this area. Our own modeling efforts are focused

on developing a bio-optical algorithm specific to this region.

 8.2 RESEARCH ACTIVITIES

We participated on three cruises during 2001 (April, July

and October) that covered the main stem of Chesapeake Bay.

Sampling consisted of in-situ radiometric profiles and the

collection of discrete samples for measuring absorption

spectra, pigments by HPLC, and chl-a by fluorometry. Ten

stations were visited on each cruise for bio-optical

measurements. Discrete samples were collected from

additional stations throughout the Bay for fluorometric chl-a

analyses.

Spectral absorption coefficients (300-800 nm) of

particulate material were measured by concentration on

Whatman GF/F filters and analyzed on a dual beam

spectrophotometer (Shimadzu UV-2401PC UV-VIS) before

and after extraction with hot methanol for determination of

absorption coefficients due to pigmented (aph) and nonpigmented (ad) components. Seawater samples collected for

analysis of the dissolved fraction were filtered through 0.2 • m

cellulose syringe filters. Absorbance of the filtrate was

measured in 10 cm cuvettes on an HP8452A Diode Array

spectrophotometer at wavelengths from 190 to 820 nm against

a Milli-Q water blank for the determination the absorption

coefficient due to CDOM (aCDOM).
SIMBIOS Project Annual Report

53

In-situ bio-optical profiles were obtained with a MER2040 (Biospherical Instruments, Inc.) underwater radiometer

that measured downwelling irradiance (Ed) and upwelling

radiance (Lu) at thirteen spectral bands. A MER-2041 mounted

on the deck provided simultaneous measurements of surface

downwelling irradiance. Radiometric profiles were processed

according to accepted protocols (Fargion & Mueller, 2000).

An instrument self-shading correction was applied to all

measurements of surface upwelling radiance (Lu0-

) (Gordon &

Ding, 1992, Zibordi & Ferrari, 1995, Fargion & Mueller,

2000). Discrete samples were collected for the analysis of

photosynthetic pigments by HPLC and chl-a by fluorometry.

In addition to our routine sampling, we arranged to conduct an

inter-laboratory comparison of HPLC determinations of

photosynthetic pigment concentrations with Dr. Trees'

laboratory at CHORS. Samples were collected from three

stations spanning the north-south axis of the Bay. At each

station six samples were collected by filtration using a

homogenous source. The filters were stored frozen using

accepted protocols, and analyzed at the Horn Point Laboratory

Analytical Services facility according to HPLC methods based

on Van Heukelem and Thomas (2001). Replicates were

shipped to Dr. Trees' laboratory for independent analyses. The

samples were submitted to each laboratory using a coding by

station not provided to the analyst to assure "blind"

comparisons. Samples for the inter-laboratory comparison

were collected on the July and October cruises of 2001, and

will be collected on three cruises in 2002. Reporting of HPLC

results will be made to the SIMBIOS program office by the

PIs. The radiometric measurements made in Chesapeake Bay

were expanded in 2001 with the purchase of three new above

water and in-water instruments, including (1) Satlantic

MicroPro (free-fall profiler); (2) Satlantic HyperTSRB

(tethered surface buoy); and (3) ship-mounted Satlantic

SeaWiFS Aircraft Simulator (SAS III) sensors (above water

measurements). The main acquisition in support of SIMBIOS

is the MicroPro profiler that has advantages over the MER due

to the small diameter and ease of deployment. The instrument

self shading correction using the MER can reach 50% in the

upper Chesapeake Bay, and the smaller diameter of the

MicroPro should lessen this problem. We have made nearly

simultaneous measurements from the new instruments and the

MER 2040/41 during the summer and fall cruises in 2001, and

we will be continuing these comparative measurements in

2002 and 2003.

In situ data collected during the 1998-2000 sampling

period were used for validation of SeaWiFS products for the

same period. SeaWiFS Level 1a HRPT images of the

Chesapeake Bay / MAB region from were processed to Level

2 using the Siegel NIR atmospheric correction option utilizing

bands 7 and 8. Images were processed with all of the standard

masks, including land, cloud, glint, stray light, and high light.

To avoid pixels with large atmospheric paths, all pixels with

satellite zenith angles greater than 56.0° were masked. Direct

comparisons were made between satellite and in-situ

measurements of LWN, chl-a and K490. SeaWiFS LWN were

compared with in-situ LWN calculated as:

LWN = Fo (LW / ES) = Fo (RRS) (1)

where Fo is the extraterrestrial solar irradiance and LW is an

instrument self-shade corrected value. Valid match-ups

between SeaWiFS observations and in-situ measurements of

LWN and Kd490 were limited to a time window of ± 3 hours.

Chl-a match-ups were considered valid if the satellite

observation and in-situ sample collection occurred on the

same day. Time series of chl-a from in-situ, aircraft, and

SeaWiFS sources were compared to determine if SeaWiFS

resolved spatial and temporal variability of chl-a present in the

in-situ data. Additional measurements made during the 1995-

2000 Chesapeake Bay cruises include a large dataset of

primary productivity observations that will be submitted to

SIMBIOS in the coming year. These data have been used in

the development of a depth-integrated primary productivity

model for Chesapeake Bay (Harding et al., 2001). We are

extending the database of observations used to develop the

model to include aircraft and satellite ocean color estimates of

chl-a. The increased spatial and temporal resolution of these

remote sensing data sources is expected to improve the

performance of the model.

8.3 RESEARCH RESULTS

Relationships between Inherent and Apparent Optical

Properties

Our data analysis has focused on the relationships

between measured inherent and apparent optical properties in

turbid Case 2 waters. Spectral absorption coefficients in this

region indicated that the relative importance of CDOM,

phytoplankton and detrital/tripton absorption to the total water

column absorption (aT) varied along the salinity gradient (Fig.

1). aCDOM exhibited a strong conservative relationship, while ad

decreased exponentially with salinity. aph exhibited no strong

relationship with salinity except for the consistent appearance

of a maximum at salinities of 10-15 psu. This maximum of aph

may correspond to an alleviation of light-limitation along the

main stem axis of the Bay as ad decreased in a seaward

direction. Detrital absorption dominated the total water

column absorption in the upper Bay. In the mid- and lower

Bay aph generally was greater than aCDOM and ad, although the

differences in the relative importance of each fraction were

usually small. Absorption coefficients in the MAB waters

were an order of magnitude lower and were found to be

CDOM-dominated, especially at the blue wavelengths.
SIMBIOS Project Annual Report

54

Figure 8.1: Distribution of absorption and attenuation coefficients with salinity in the Bay and offshore waters: (a) aCDOM at 440

nm, (b) ad at 440 nm, (c) aph at 440 nm, and d) Kd at 490 nm. Symbols indicate the following regions: = Chesapeake Bay,  =

Inshore,  = Offshore.
SIMBIOS Project Annual Report

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Figure 8.2: Spectral aT, Kd, RRS and bT for regions within Chesapeake Bay and adjacent MAB waters: upper Chesapeake Bay

(solid), mid-Chesapeake Bay (dotted), lower Chesapeake Bay (dash), inshore MAB (dash-dot), and offshore MAB (dash-dotdot-dot). (a-b) mean and standard deviation of total absorption coefficients, (c-d) mean and standard deviation of diffuse

attenuation coefficient, and (e-f) mean and standard deviation of bT calculated from Equation 2. Mean values represent data

collected on cruises in 1996-2000.
SIMBIOS Project Annual Report

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Figure 8.3: (a) Rmax for the in-situ radiance data vs. in situ chl-a. Symbols indicate the maximum ratio used for each station:

RRS443 / RRS555 (), RRS490 / RRS555 (o) , and RRS510 / RRS555 (x). Solid line represents OC4v.4. (b) Comparison of OC4v.4

calculated from in-situ radiance data and the measured chl-a for each station for Chesapeake Bay/inshore MAB (x) and

offshore MAB (o). (c) Comparison of in-situ and SeaWiFS-derived chl-a for Chesapeake Bay/inshore MAB (x) and offshore

MAB (o). Solid line is the 1:1 line. Dashed lines indicate the SeaWiFS uncertainties goal for chl-a of ± 35%. Data from cruises

in 1996 – 2000.
SIMBIOS Project Annual Report

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Regional trends in the magnitude and spectral shape of RRS

and Kd reflect the interplay of both absorption and scattering

(Fig. 8.2). Kd decreased in magnitude and flattened out with

distance down the Bay and offshore, similar to the pattern

observed for aT. Maxima in RRS shifted from 589 nm in the

Bay to 560 nm in the inshore MAB and 443 nm in the offshore

MAB, reflecting the progressive decrease in aT with distance

offshore. However, the magnitude of RRS did not decrease

sequentially down the Bay. The highest RRS values were found

in the upper Bay. RRS increased from relatively low values in

the mid-Bay to higher values in the lower Bay and inshore

waters. Scattering in the water column likely controls

variability in the magnitude of RRS. In order to obtain an

estimate of particle scattering we used in-situ optical data as

inputs to a model relating diffuse attenuation to absorption and

scattering (Kirk, 1981, 1984, 1994). Based on Monte Carlo

simulations Kirk (1994) derived:

1/ 2

1 0.245 ( ) 



 





≅ +

T

T

T

d m

a

b

a

K z (2)

for turbid waters where Kd (zm) is the attenuation coefficient at

the midpoint of the euphotic zone. The results for Chesapeake

Bay indicated little variability of bT with wavelength. Mean

values of bT were 10 m-1 in the upper Bay, 5 m-1 in the lower

Bay and inshore waters, and an order of magnitude lower in

the offshore waters (Fig. 8.2). A great deal of variability exists

for each region, but the mean values are in agreement with a

previous study in Chesapeake Bay (Gallegos et al., 1990), and

provide an indication of spatial variability in the relative

importance of scattering in this region.

OC4v.4 Performance in Chesapeake Bay

In-situ radiance data from Chesapeake Bay / MAB were used

as inputs to OC4v.4:

2 33 4 max (0.366 3.067 1.930 max 0.649 max 1.532 max 10.0 RR R R C −+ + − = (3)

where ) log10( 510

555

490

555

443 Rmax = RRS 555 > RRS > RRS selects the

maximum of the three RRS ratios (443/555, 490/555, and

510/555) (O’Reilly et al., 2000). Comparisons of OC4v.4 –

modeled chl-a with measured chl-a indicated a tendency for

OC4v.4 to overestimate chl-a in the Bay (mean ratio = 1.72 ±

1.44) (Fig. 8.3; Table 8.1). In the offshore waters dominated

by CDOM there was a large bias in the OC4v.4-modeled chl-a

(mean ratio 2.72 ± 1.43). Standard deviations of the ratios

indicate that under the best circumstances (i.e., no uncertainty

in the radiance observations) uncertainty of chl-a predicted

from OC4v.4 was at least a factor of two.

SeaWiFS – In Situ Match-ups

Comparisons of in-situ and SeaWiFS LWN showed a

strong wavelength dependency (Table 8.2). SeaWiFS greatly

underestimated LWN at 412 and 443 nm, and the percentage of

negative LWNs was large. The number of negative values

decreased significantly at 490 and 510 nm, and there were no

match-up stations that had negative SeaWiFS values at 555

nm. All of the negative LWNs occurred at Chesapeake Bay

stations. In the MAB SeaWiFS LWNs were within ten percent

of in-situ values at 490, 510 and 555 nm, but significant errors

remained at 412 and 443 nm. The mean ratio for Kd490 also

improved from 0.840 in the Bay to 0.965 in the MAB.

Errors in the satellite-derived LWNs introduced additional

errors to the SeaWiFS-derived chl-a values. The bands usually

used in OC4v.4 are 510 nm in the Bay and 490 nm in the

MAB. These bands exhibited biases in these waters of ~22%

and ~7%, respectively (Table 8.2). Comparisons of OC4v.4 –

modeled chl-a and SeaWiFS chl-a with in situ values showed

that chl-a values estimated by SeaWiFS were even higher than

those estimated from OC4v4 alone (Table 8.1), reflecting the

large bias in SeaWiFS LWN at 510 nm. In the MAB where the

bias in LWN was relatively small, the OC4v.4 and SeaWiFS

chl-a were similar. While the OC4v.4 algorithm avoids using

wavebands with the greatest bias due to atmospheric

correction problems, underestimates of LWN by SeaWiFS still

introduce additional bias to the estimates of chl-a in the Bay.

Spatial and Temporal Variability in Chl-a – SeaWiFS, Aircraft

and Field Data

An extensive aircraft remote sensing program in

Chesapeake Bay (http://www.cbrsp.org; Harding et al., 1992,

1995) made it possible to compare spatial and temporal

variability of surface chl-a from three independent data

sources, aircraft, satellite, and ship. A time series of in-situ,

aircraft and satellite chl-a from 1998 to 2000 showed that

SeaWiFS captured seasonal and inter-annual variability of chla that was evident in the aircraft and ship data (Fig. 8.4). In

1998, chl-a from SeaWiFS showed a two to three-fold

increase during the spring bloom period (February and March)

in much of the mid- and lower Bay (Fig. 8.4). The timing and

spatial extent of the bloom were consistent with the aircraft

and ship data. By the April cruise, chl-a from satellite and ship

returned to pre-bloom levels. This level of agreement between

data sources was representative of the results for all three

years of the time series.

A salient feature of the three-year time series was that

SeaWiFS returned reasonable chl-a values for the lower Bay

and for most of the mid-Bay, but just north of latitude 38.2°N.

(between the Potomac and Patuxent R. mouths), SeaWiFS

significantly overestimated chl-a. One explanation could be

that high detrital absorption in the upper Bay result in high
SIMBIOS Project Annual Report

58

scattering and non-negligible water leaving radiances in the

NIR, leading to overestimates of chl-a in the upper Bay from

errors in the atmospheric correction that produce

underestimates of LWN at key bands from SeaWiFS. South of

latitude 38.2°N., SeaWiFS still had a tendency overestimate

chl-a by about a factor of two. There were occasional images

(~1-2 scenes each year) in which 3-4 fold increases of chl-a

occurred for a single satellite image. The cause of these

sporadic deviations is not known and should be investigated

further. However, they are anomalies in the time series.

7.4 FUTURE WORK

Validation of SeaWiFS LWN, K490 and chl-a revealed

problems in atmospheric correction and in the performance of

the bio-optical algorithm that were not unexpected for Case 2

waters. OC4v.4 - chl-a exhibited a bias to higher values due to

absorption by CDOM and detrital components that was

attributed to phytoplankton. High uncertainties resulted from

variable contributions of phytoplankton, CDOM and detrital

fractions to absorption and scattering in the water column. The

increased bias of SeaWiFS chl-a estimates relative to OC4v.4-

chl-a in Chesapeake Bay likely reflects a problem in

atmospheric correction. Even with the use of the Siegel 7/8

iteration scheme, SeaWiFS LWNs in Chesapeake Bay were still

underestimated at 412, 443, 490 and 510 nm, causing OC4v.4

to return overestimates of chl-a. The atmospheric correction

problem appeared to dominate in the upper Bay where

overestimates of chl-a were large. In the mid- and lower Bay

the bias can be attributed primarily to the performance of

OC4v.4, and secondarily to underestimates of SeaWiFS LWNs.

The atmospheric correction problem was less significant in the

offshore waters where LWN returned by SeaWiFS were in

closer agreement with in situ values. Despite the bias in chl-a,

we found that SeaWiFS did resolve relative changes in chl-a

in lower and mid-Chesapeake Bay on time scales of days to

weeks, provided the changes in chl-a were greater than the

uncertainty in the SeaWiFS chl-a estimate.

Further improvement in satellite-derived chl-a for this

region requires a bio-optical algorithm that includes terms

describing the absorption and scattering of all components.

Analysis of existing bio-optical data will continue in an effort

to appropriately parameterize a semi-analytical algorithm for

this region. Absorption has been well characterized in this

system. Scattering is obviously important to the ocean color

signal of these waters. Refinement of a region-specific biooptical model would clearly benefit from direct measurements

of scattering, as well as studies of particle size distributions

and composition. Efforts will be made to obtain these

measurements in the coming year.

REFERENCES

Bailey, S.W., C.R. McClain, P.J. Werdell and B.D. Schieber,

2000: Normalized water-leaving radiance and chl-a a

match-up analyses. In: McClain,C.R R.A. Barnes, R.E.

Eplee, Jr., B.A. Franz, N.C. Hsu, F.S. Patt, C.M. Pietras,

W.D. Robinson, B.D. Schieber, G. M. Schmidt, M. Wang,

S.W. Bailey, P.J. Werdell: SeaWiFS Postlaunch

Calibration and Validation Analyses, Part 2. NASA Tech.

Memo. 2000-206892, Vol. 10, S.B. Hooker and E.R.

Firestone, Eds., NASA Goddard Space Flight Center,

Greenbelt, Maryland, 12-29.

Fargion, G.S. and J.L. Mueller, 2000: Ocean Optics Protocols

for Satellite Ocean Color Sensor Validation, Revision 2,

NASA Tech. Memo. 2000-209966, S.B. Hooker and E.R.

Firestone, Eds., NASA Goddard Space Flight Center,

Greenbelt, Maryland, 184 p.

Gallegos, C.L., D.L. Correll, and J.W.Pierce, 1990: Modeling

spectral diffuse attenuation, absorption, and scattering

coefficients in a turbid estuary. Limnol. Oceanogr., 35,

1486-1502.

Gordon, H.R. and A. Morel, 1983: Remote assessment of

ocean color for interpretation of satellite visible imagery,

a review, in R.T. Barber, N.K. Mooers, M.J. Bowman and

B. Zeitzschel, Eds., Lecture Notes on Coastal and

Estuarine Studies, Springer-Verlag, New York, 114 pp.

Gordon, H.R. and K. Ding, 1992: Self-shading of in-water

optical instruments. Limnol. Oceanogr., 37, 491-500.

Harding, L.W., Jr., E.C. Itsweire and W.E. Esaias, 1992:

Determination of phytoplankton chl-a in the Chesapeake

Bay with aircraft remote sensing. Rem. Sens. Environ.,

40, 79-100.

Harding, L.W., Jr., E.C. Itsweire and W.E. Esaias, 1995:

Algorithm development for recovering chl-a in the

Chesapeake Bay using aircraft remote sensing, 1989-

1991. Photogramm. Eng. Rem. Sens., 61, 177-185.

Kirk, J.T.O., 1981: Monte Carlo study of the nature of the

underwater light field in, and the relationships between

optical properties of, turbid yellow waters. Aust. J. Mer.

Freshwaters Res. 32, 517-532.

Kirk, J.T.O., 1984: Dependence of relationship between

inherent and apparent optical properties of water on solar

altitude. Limnol. Oceanogr., 29(2), 350-356.
SIMBIOS Project Annual Report

59

Kirk, J.T.O., 1994: Characteristics of the light field in highly

turbid waters: A Monte Carlo study, Limnol. Oceanogr.,

39(3), 702-706.

Morel, A. and L. Prieur, 1977: Analysis of variations in ocean

color. Limnol. Oceanogr., 22, 709-722.

O’Reilly, J.E., S. Maritorena, B.G. Mitchell, D.A. Siegel, K.L.

Carder, S.A. Garver, M. Kahru, and C. McClain, 1998:

Ocean color algorithms for SeaWiFS, J. Geophys. Res.,

103(C11), 24,937-24,953.

O’Reilly, J.E. et al., 2000: SeaWiFS Postlaunch Calibration

and Validation Analyses, Part 3. NASA Tech. Memo.

2000-206892, Vol. 11, S.B. Hooker and E.R. Firestone,

Eds., NASA Goddard Space Flight Center, Greenbelt,

Maryland, 49 p.

Siegel, D.A., M. Wang, S. Maritorena and W. Robinson,

2000: Atmospheric correction of satellite ocean color

imagery: the black pixel assumption. Appl. Opt., 39,

3,582-3,591.

Van Heukelem, L and C.S. Thomas, 2001: Computer-assisted

high-performance liquid chromatography method

development with applications to the isolation and

analysis of phytoplankton pigments. J. Chromatogr. A,

920, 31-49.

Zibordi, G. and G.M. Ferrari, 1995: Instrument self-shading in

underwater optical measurements: experimental data.

Appl. Opt., 34, 2750-2754.

This research was supported by

SIMBIOS NASA contract # 00195

PEER REVIEWED PUBLICATIONS

Harding, L.W., Jr., W.D. Miller, R.N. Swift and C.W. Wright.

2001: Aircraft remote sensing, in J.S. Steele, S. Thorpe,

and K. Turekian, Eds., Encyclopedia of Ocean Sciences,

Academic Press, London, 113-122.

Harding, L.W., Jr., M.E. Mallonee, M.E., and E.S. Perry,

2001: Toward a predictive understanding of primary

production in a temperate, partially stratified estuary. Est.

Coast. Shelf Sci. (in press).

Magnuson,A, L.W. Harding, Jr., and M.E. Mallonee, 2001:

Bio-optical and remote sensing observations in

Chesapeake Bay (in prep.).

PRESENTATIONS

Magnuson,A, L.W. Harding, Jr., and M.E. Mallonee, 2001:

Satellite and aircraft remote sensing in Chesapeake Bay.

Poster presented to the SIMBIOS Science Team Meeting

at Goddard Space Flight Center, Greenbelt, Maryland,

January 2001.

Harding, L.W., Jr., 2001: Aircraft remote sensing of ocean

color in Chesapeake Bay to estimate chlorophyll and

primary productivity. Presentation to EPA Environmental

Monitoring and Assessment (EMAP) Conference,

Pensacola Beach, Florida, 23-27 April 2001.

Magnuson,A, L.W. Harding, Jr., and M.E. Mallonee, 2001:

Chesapeake Bay as seen by SeaWIFS. Presented to the

SeaWiFS Science Team Meeting, San Diego, California,

May 2001.
SIMBIOS Project Annual Report

60

Figure 8.4: Time series of regional mean chl-a observations from cruises, aircraft and SeaWiFS imagery in 1998. Regional mean

values were calculated from surface chl-a maps interpolated from in-situ station data (filled o) and aircraft transects (o), and

SeaWiFS imagery (thick line). In-situ data symbols include error bars indicating the length of the cruise. An average of all data

north of 38.2°N is provided in the upper panel. Average values for 0.2° latitude bands are provided in all remaining panels.
SIMBIOS Project Annual Report

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Table 8.1: Comparison of the mean ratio of chl-a calculated from OC4v.4, using in situ radiance measurements, and in situ chla, with the mean ratio of same day match-ups of SeaWiFS-derived and in situ chl-a.

OC4v.4a

In Situ

SeaWiFSb

Region In Situ

Mean (+ sd)) Mean (+ sd)

Chesapeake Bay/

Inshore MAB

1.77 (+ 1.44)

n = 260

2.10 (+ 1.69)

n = 235

Offshore MAB 2.72 (+ 1.43)

n = 64

2.69 (+ 0.97)

n = 7

Overall 1.95 (+ 1.36)

n = 324

2.12 (+ 1.66)

n = 242

a Ratio of OC4v.4 chl-a calculated using in-situ radiances and in situ chl-a. All in-situ data obtained on cruises in 1996-2000. b

 Match-ups of SeaWiFS 3x3 pixel mean and in-situ chl-a during 1998-2000.
SIMBIOS Project Annual Report

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Table 8.2. Statistics for match-ups between in situ and SeaWiFS observations (3x3 pixel mean) of LWN, Kd490

and chl-a for Chesapeake Bay and MAB waters. Match-ups of LWN and Kd490 were limited to in situ

observations made within ±3 hours of the satellite pass. Chlorophyll match-ups consisted of satellite and in situ

observations on the same day. The differences in the number of LWN match-ups at each waveband reflect the

omission of match-ups with negative LWN at lower wavebands. SeaBASS values included for comparison.

Region Parameter Mean Standard Coefficient Number of Range of

 Ratio Deviation of Variance Match-Ups In Situ Values

Chesapeake Bay/

Inshore MAB

LWN412 0.240 0.044 0.185 3 0.014-0.707

LWN443 0.598 0.251 0.419 4 0.011-0.988

LWN490 0.790 0.173 0.219 7 0.032-1.45

LWN510 0.782 0.331 0.423 8 0.053-1.63

LWN555 0.962 0.273 0.284 9 0.223-2.01

Kd

490 0.840 0.474 0.565 8 0.578-2.436

Chl a 2.100 1.69 0.805 235 1.1-38.2

Offshore MAB

LWN412 0.378 0.249 0.658 7 0.141-2.145

LWN443 0.776 0.327 0.422 7 0.243-1.916

LWN490 0.928 0.249 0.269 7 0.407-1.464

LWN510 0.983 0.224 0.228 7 0.394-1.589

LWN555 0.957 0.235 0.246 7 0.266-1.838

Kd

490 0.965 0.379 0.393 7 0.028-.468

Chl a 2.69 0.97 0..361 7 .11-.27

SeaBASS1 LWN412

0.856 0.222 0.259 119 0.330-3.238

LWN443

0.957 0.246 0.256 141 0.219-2.709

LWN490

0.973 0.206 0.211 153 0.252-1.695

LWN510

1.075 0.236 0.219 136 0.215-0.127

LWN555

1.106 0.279 0.252 149 0.127-0.538

Kd

490

1.225 0.287 0.222 125 0.018-0.340

Chl a 1.061 0.498 0.434 76 0.062-4.650

1

Bailey et al. (2000)
SIMBIOS Project Annual Report

63

Chapter 9

Refinement of Protocols for Measuring the Apparent Optical

Properties of Seawater

Stanford B. Hooker

NASA/GSFC/SeaWiFS Project, Greenbelt, Maryland

Giuseppe Zibordi and Jean-François Berthon

JRC/IES/Inland and Marine Waters, Ispra, Italy

André Morel and David Antoine

CNRS/UPMC/Laboratoire d’Océanographie de Villefranche, Villefranche-sur-Mer, France

9.1 INTRODUCTION

Ocean color satellite missions, like the Sea-viewing Wide

Field-of-view Sensor (SeaWiFS) or the Moderate Resolution

Imaging Spectroradiometer (MODIS) projects, are tasked with

acquiring a global ocean color data set, validating and

monitoring the accuracy and quality of the data, processing the

radiometric data into geophysical units using a set of

atmospheric and bio-optical algorithms, and distributing the

final products to the scientific community. The long-standing

objective of the SeaWiFS Project, for example, is to produce

water-leaving radiances to within 5% absolute (Hooker and

Esaias 1993). The accurate determination of upper ocean

apparent optical properties (AOPs) is essential for the

vicarious calibration of ocean color data and the validation of

the derived data products, because the sea-truth measurements

are the reference data to which the satellite are compared

(Hooker and McClain 2000). The uncertainties associated with

in situ AOP measurements have various sources, such as, the

deployment and measurement protocols used in the field, the

absolute calibration of the radiometers, the environmental

conditions encountered during data collection, the conversion

of the light signals to geophysical units in a data processing

scheme, and the stability of the radiometers in the harsh

environment they are subjected to during transport and use.

In recent years, progress has been made in estimating the

magnitude of some of these uncertainties and in defining

procedures for minimizing them. For the SeaWiFS Project, the

first step was to convene a workshop to draft the SeaWiFS

Ocean Optics Protocols. The protocols adhere to the Joint

Global Ocean Flux Study (JGOFS) sampling procedures (Joint

Global Ocean Flux Study 1991) and define the standards for

optical measurements to be used in SeaWiFS radiometric

validation and algorithm development (Mueller and Austin

1992). The protocols are periodically updated as deficiencies

are identified and outstanding issues are resolved (Mueller and

Austin 1995, and Mueller 2000). The follow-on inquiries into

controlling uncertainties investigated a variety of topics. The

SeaWiFS Intercalibration Round-Robin Experiment

(SIRREX) activity demonstrated that the uncertainties in the

traceability between the spectral irradiance of calibration

lamps were approximately 1.0%, and the intercomparisons of

sphere radiance was approximately 1.5% in absolute spectral

radiance and 0.3% in stability (Mueller et al. 1996). The first

SeaWiFS Data Analysis Round Robin (DARR-94) showed

differences in commonly used data processing methods were

about 3–4% of the aggregate mean estimate (Siegel et al.

1995). Hooker and Aiken (1998) made estimates of radiometer

stability using the SeaWiFS Quality Monitor (SQM), a

portable and stablelight source, and showed the stability of

radiometers in the field during a 36-day deployment was on

average to within 1.0% (although some channels occasionally

performed much worse). More recently, Hooker and

Maritorena (2000) quantified differences in the in-water

methods and techniques employed for making radiometric

measurements and demonstrated a total uncertainty in the

measurement of in-water AOPs at approximately the 3% level.

9.2 RESULTS

Open Ocean

The SeaWiFS Field Team has combined the collection of

ground-truth observations with specific experiments to
SIMBIOS Project Annual Report

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investigate the sources of uncertainties in AOP measurements.

Many of the experiments and data collection opportunities

took place during several Atlantic Meridional Transect (AMT)

cruises on board the Royal Research Ship James Clark Ross

(JCR) between England and the Falkland Islands (Aiken et al.

2000). The majority of the AMT data set was from Case-1

conditions and took place from September 1995 to June 1999.

Water-leaving radiances can be derived by extrapolating

in-water measurements taken close to the sea surface or they

can be obtained directly from above-water measurements.

More recently, additional experiments and data collection

activities have been executed to extend the AMT

predominantly in-water data set to other open ocean areas and

above-water methods. These include a) the Productivité des

Systèmes Océaniques Pélagiques (PROSOPE) cruise, which

took place between 4 September and 4 October 1999; b) a

cruise into the Mid-Atlantic Bight, which took place between

24 April and 3 May 2000, and c) two cruises into Exuma

Sound, which took place between 24 February and 1 March

2000, as well as, 22 February and 1 March 2001.

Although it has not been as extensively validated as the

in-water approach, above-water methods for vicarious

calibration remain nevertheless attractive, because a) the data

can presumably be collected more rapidly and from a ship

underway, and b) the frequently turbid and strongly absorbing

waters in shallow Case-2 environments impose severe

limitations on in-water measurements, particularly because of

the instrument self-shading effect.

Above- and in-water measurements of LW (λ) can be

related to one another using the pointing geometry of the

sensors, the sun geometry, the (surface) interface transmission,

and the so-called Q-factor. The Q-factor relates the upward

radiance field below the surface with that exiting the surface,

the angular bidirectional dependency of these fields, and the

transformation of radiance or irradiance into reflectance

(Morel and Gentili 1996). In-water measurements of the Qfactor with nadir-pointing instruments (the usual case) are

denoted Qn(λ) and are calculated as the ratio of the upward

irradiance to the upwelling radiance, Qn(λ) = Eu(λ)/Lu(λ).

The data from the most recent cruises agreed well with

the AMT data set in terms of the accuracy of the in-water

AOP measurements and the uncertainties in inverting the

optical data into chlorophyll a concentration using a standard

ocean color algorithm, but they also confirmed two

inconsistencies in the AMT data set. The first inconsistency

was the amplitude and spectral dependence of Qn(λ) did not

agree with theoretical studies. To more completely investigate

this result, special so-called Q-factor experiments were

conducted during the PROSOPE and Mid-Atlantic Bight

cruises. In these experiments, sequential casts were made

during clear-sky conditions in homogeneous, Case-1 waters

during an extensive part of the afternoon (i.e., during a

significant change in the solar zenith angle, θ). Figure 9.1

shows the distribution of Qn(λ) as a function of θ. Although

the general shape of increasing Qn(λ) with increasing solar

zenith angle is in agreement with theory, the amplitudes are

incorrect, and more important, the wavelength dependence is

inverted: Qn(λ) decreases with increasing λ, whereas theory

predicts it should increase.

Figure 9.1: The distribution of Qn(λ) as a function of the

solar zenith angle.

The second inconsistency was the above-water LW (λ)

values collected from a large research vessel disagreed with

the in-water values, which were collected simultaneously, by

as much as 13–27%. All the field instruments were

intercalibrated, and the in-water instruments were floated far

away from the ship before any data were recorded, so the most

likely explanation for the disagreement was platform

contamination in the above-water data.

This possibility was analyzed by devising a diagnostic

parameter, r(865), based on the principles involved in the

formulation of two above-water methods: Morel (1980) and

the SeaWiFS Ocean Optics Protocols (Mueller and Austin

1995), hereafter referred to as M80 and S95, respectively.

When using an above-water method, the total radiance

measured above the sea surface, LT(λ), includes the wanted

information, LW (λ), plus a contamination term, ∆L, originating

from light reflected by the sea surface and into the sensor:

LT (λ) = LW (λ) + ∆L, (1)

where the pointing requirements have been omitted.
SIMBIOS Project Annual Report

65

The processing of above-water data consists of removing

the contamination term, ∆L in (1), which adds to the marine

signal and originates from reflections at the air–sea interface.

The sky radiance, Li(λ), reflected off the wave-roughened

surface into the detector is a priori at the origin of the ∆L

signal. Reflected radiation from the sampling platform is,

however, another source of contamination. Even if only the

sky reflection is considered, its contribution to LT (λ) is always

important. For Case-1 waters, in the near-infrared domain

(e.g., at 780 and 865 nm) where the sea is essentially black,

this contribution amounts to 100%, and decreases toward short

wavelengths where the diffuse ocean reflectance departs from

zero. In Case-2 waters, particularly when the sediment load is

high, the water reflectance may deviate from zero in the near

infrared and contribute to the ∆L signal.

The M80 glint correction method is based on the

existence of a black target in the near-infrared region at a

reference wavelength, λr. The above-water radiance measured

at λr is entirely due to surface reflection, and this estimate is

extended over the whole spectrum by using the spectral

dependence of the incident sky radiance measured in the

direction appropriate for reflection from the sea surface.

Estimated glint is subtracted from the total signal to recover

LW (λ). The S95 method makes use of the same set of

measurements, but they are used differently. The glint is

removed through a constant interface reflectance factor, ρ

which is applied to all the spectral sky radiances; in general, ρ

depends on the capillary wave slopes, and, thus, on wind

speed (Austin 1974 and Mobley 1999).

A comparison of the output of the M80 and S95

correction methods allows the detection of any ship

contamination in the LT (λ) signal in Case-1 waters. This is

because the M80 method is sensitive to, and, thus, is able to

identify a ship perturbation, whereas the S95 method, based on

a theoretical value of the reflectance factor, will just ignore it.

The presence of a ship perturbation can be detected with the

ratio

r(865) = LT (865) / Lr (865)

ρ

(2)

where the numerator comes from M80 and the denominator

from S95. Under normal circumstances, i.e., in the absence of

a ship perturbation, r(865) = 1, within the accepted variance

(and provided that ρ is given a correct value). Any other

reflected radiation added to the sky-reflected radiation will

lead to r(865) > 1, and the departure from unity is an estimate

of this effect. Figure 9.2 presents the r(865) values for the

PROSOPE cruise plotted as a function of the pointing angle of

the above-water radiometers with respect to the side of the

ship, α.

Figure 9.2: The distribution of r(865) as a function of

α (negative α values are towards the bow and positive

α values towards the stern). Out of the total data set of

128 casts, 19 were in overcast conditions and 109

were in clear-sky conditions; overcast data are not

shown as separate symbols, because they fall within a

narrow range (slightly larger than 1) and all at α = 0,

so they would obscure the clear-sky results. The inset

panel shows the pointing angle of the above-water

radiometers with respect to the ambient swell (β), and

the angle of the sun with respect to the bow (γ).

Although the PROSOPE data set was composed of 137

simultaneous above- and in-water casts, 9 were excluded

because of unanticipated cloud interference, ship movement

during the cast, etc. The remaining 128 casts provide a good

distribution of data with respect to α, and show that when the

instrument is pointed perpendicular to the side of the ship (α =

0), the r(865) values are a little larger than 1, which suggests a

reduced contamination by the ship. As the radiometers are

pointed more and more towards the bow or stern, that is when

the distance from the ship to the surface spot decreases, r(865)

dramatically increases, reaching values as high as 4–5 when

α ±60o

. These large ratios indicate the radiation reflected by

the surface and seen by the sensor is largely dominated by that

originating from the superstructure.

These high values, however, are not observed in a

systematic manner. For example, when α = 40o or 60o, r(865)

values span the interval 1–5, which deserves another kind of

analysis, involving γ, i.e., the angle between the center line of

the ship and the position of the sun (Fig. 2 inset panel).

According to the sign of γ the port side of the ship (where the
SIMBIOS Project Annual Report

66

above-water radiometers were installed) is, or is not,

illuminated by the sun, which makes a difference in the

intensity (and spectral composition) of the light reflected from

the superstructure.

An analysis of r(865) as a function of γ shows the

contamination effects of the ship are reduced when the side

from which the sensor is operated is in shadow, so that the

superstructure is only illuminated by the sky radiation (or by

uniform clouds); the contamination increases when the port

side is sunlit, or if the bridge is sunlit (when γ is small). The

geometrical aspect of the contamination is not surprising;

more surprising is the importance of the effect and its

complexity related to the shape of the superstructure. As with

many ships, elements of the forward superstructure on the

research vessel used during PROSOPE sloped to the sides of

the vessel, which provided reflection opportunities under a

variety of sun geometries with respect to the bow.

Although not as significant as the sun geometry, the

effects of surface gravity waves on the above-water

measurements were quantifiable: a) measurements along the

wave troughs were a significant local minimum, which means

they were radiometrically darker than the wave crests at all

wavelengths; b) the darkening effect was the largest at 412

and 555 nm; and c) at 510–555 nm, a converse trend (i.e., a

brightening) seemed to occur.

Coastal Ocean

The PROSOPE above-water data were collected with

instruments fixed to the pointing assembly. Although the

instruments could be pointed to arbitrary angles in the vertical

and horizontal planes, they were sufficiently massive that they

could not be safely operated at very high points on a ship's

superstructure, particularly in high sea states. In anticipation

of working on smaller boats in coastal waters, a very small

system that could be gimbaled and, thus, would not be as

negatively influenced by ship motion was developed. This

required the SeaWiFS Field Team to fund the development of

a new class of optical sensors, which are significantly smaller

and lighter than those currently available. These new sensors

are manufactured by Satlantic, Inc. (Halifax, Canada) and are

referred to as the OCR-500 series of instruments.

The new above-water instrument is called the micro

Surface Acquisition System (microSAS) and is pictured in

Fig. 9.3. The above-water radiometers are mounted on

movable plates contained within a gimbal. The plates can be

set to an arbitrary vertical (nadir or zenith) angle with respect

to the horizontal plane between 0–50o (most data are currently

collected at 40o). In addition, the entire sensor assembly can be

rotated to arbitrary azimuthal viewing angles to within 0.5o. A

sun compass at the top of the assembly allows for easy

pointing of the instrument to ±90o (in 15o increments) with

respect to the solar plane. The ballast for the gimbal contains a

compass with built-in tilt and roll sensors. The latter ensures

confirmation of the vertical orientation of the sensors during

data acquisition, and permits fine tuning (ballasting and

tensioning) of the gimbal structure in any sea state.

Figure 9.3: The microSAS instrument composed of the

upward-pointing Li sensor and the downward-pointing LT

sensor. The ballast, containing the compass with tilt and

roll sensors, is the large black cylinder below the gimbal

plane.

The Italian Coastal Atmosphere and Sea Time-Series

(CoASTS) project is a cooperative activity between the

European Joint Research Centre (JRC) and the Italian National

Research Council (CNR). As part of the field campaigns,

which started in October 1995 and continues to-date,

atmospheric and marine measurements are periodically

performed at the Acqua Alta Oceanographic Tower (AAOT).

The tower is located in the northern Adriatic Sea

approximately 15 km southeast of the city of Venice in

approximately 17 m of water (Zibordi et al. 1999).

The SeaWiFS Photometer Revision for Incident Surface

Measurements (SeaPRISM) was conceived by the SeaWiFS

Field Team, and developed by the JRC and CIMEL

Electronique (Paris, France). SeaPRISM is based on a CE-318

sun photometer, which is an automatic system that measures

the direct sun irradiance plus the sky radiance in the sun and

almucantar planes (Hooker et al. 2000). The revision to the

CE-318 that makes the instrument useful for ocean color
SIMBIOS Project Annual Report

67

activities is a capability for measuring the sea surface using

the M80 and S95 methods. What makes this instrument

particularly powerful is it can operate autonomously, so a

sampling site can be continuously monitored in between field

campaigns that completely characterize the bio-optical

conditions of the site.

A prototype SeaPRISM was successfully deployed for a

one-year period at the AAOT which was compared to

simultaneous deployments of an in-water system (Zibordi et

al. 2002). The SeaPRISM measurements showed a slope in the

least-squares linear regression fit equal to 1.05 with a

determination coefficient of 0.99. More specifically, the

intercomparisons exhibited average unbiased percentage

difference (UPD)1

 values of 6.9, 7.2, and 23.0% at 440, 500,

and 670 nm, respectively; the intercomparison of the waterleaving radiance ratios, LW (440)/LW (500), exhibited a UPD of

5.3%.

 

Figure 9.4: The extensible deployment system used at the

AAOT showing a) the box frame fully extended with the

microSAS instruments mounted at the end, and b) the

support system. The box frame is made up of tubular

sections commonly used in the construction of aerial

towers. The small cross section of the frame, and the fact

that is was painted black, ensured a minimum

perturbation by the frame on the surface of the water.

The success of the prototype SeaPRISM system has led to

a plan to deploy a small number of production SeaPRISM

units at a variety of offshore oil and gas platforms in the

Adriatic Sea south of the AAOT. The towers are in different

water depths and typical water regimes than the AAOT, so the

small network will provide an unprecedented description of

the coastal ocean and atmosphere with autonomous

instruments. The above-water results achieved with the open

ocean analyses (Fig. 9.2), however, suggest an analysis of

 

1

 The UPD between N realizations of two data products FA and

FB

 is computed as 

 

200

N

F A(λ )−F B(λ)

F A(λ )+F B(λ) i =1

N∑ .

platform perturbations on the above-water measurements is

needed.

In preparation for a tower perturbation experiment, the

JRC developed an extensible deployment system for a

lightweight above-water system (Fig. 9.4). The extensible

system was composed of a tubular box frame that was

connected together with prefabricated sections. The box frame

was approximately 25 m long and could be extended 11 m

away from the tower and still remain rigid (at a 10 m

extension, the frame sagged approximately 1o). The microSAS

instrument was mounted at the end of the frame, and was

positioned with respect to the sun before the frame was

extended the desired distance away from the tower. The

gimbal ensured the sensors achieved a horizontal reference (to

within ±0.5o) after the frame was positioned. The frame was

moved in and out by hand, guided by rollers mounted within a

series of square supports.

The basic experimental plan was to make a series of

above-water measurements in 1 m increments with respect to

the tower (usually 10 incremental measurements were made),

all the while observing the pointing requirements with respect

to the solar plane (90o with respect to the sun). The latter

ensured that a variety of viewing distances with respect to the

tower base were collected. The experiment took place from

18–29 June 2001 during predominantly clear skies. A total of

42 experiments were conducted composed of 435 individual

above-water casts (3 min measurement sequences).

The space series of sequential measurements will be

compared to the farthest viewing point to see if there is an

increasing perturbation in the above-water data as the

measurements are made closer and closer to the tower base.

The above-water data will also be compared to a variety of inwater measurements (including discrete water filtration

samples, inherent optical properties, AOPs, etc.) taken at the

same time; these data will also be used to determine the

homogeneity of the water during each experiment.

Laboratory Measurements

If a total 5% uncertainty level is to be maintained for a

vicarious calibration exercise (remote plus in situ

instrumentation), approximately half of the uncertainty

budget, i.e., 2.5%, is available for the ground-truth component

(actually if quadrature sums are used, the ground-truth

component is closer to 3.5%). This means individual

contributions of uncertainty must be at approximately the 1%

level, which is a state-of-the-art objective. In the investigation

of laboratory sources of uncertainty, two uncertainty

thresholds were considered: 2.5% as a hoped for minimum

(because it represents almost all of the ground-truth

uncertainty budget), and 1.0% as a needed goal (because it
SIMBIOS Project Annual Report

68

permits some expansion in the other components of the total

uncertainty budget).

The poor agreement between in situ and theoretical values

of Qn(λ) (Fig. 9.2) suggests there could be a problem with the

in-water data. The accuracy of an AOP determination is a

function of the quality of the observational measurement, the

data acquisition methodology, and the data processing method

employed. The former includes the accuracy of the optical

calibration and the radiometric stability of the instruments

while they are being used in the field. The data acquisition

programs have been rigorously reviewed and tested (including

comparisons with commercial software) and show no evidence

of data corruption. The stability of the radiometers has been

repeatedly measured and intercompared in the field–-with the

exception of early instruments constructed with so-called “soft

filters” (Hooker and Aiken 1998), they are stable to within 1%

and intercompare during simultaneous casts to within 2%

(Hooker and Morel 2001). This leaves only optical calibration

and data processing as potential problems. To investigate the

possible importance of data processing methods on the final

data products, a round-robin intercomparison was organized

(Hooker et al. 2001a). Three processors from three different

groups were intercompared: the JRC (J), Satlantic (S), and the

SeaWiFS Project or GSFC (G). The focus of the round-robin

study was the estimation of a variety of commonly used data

products derived from two different classes of in-water optical

instruments. Eleven parameters important to bio-optical

analyses were intercompared. The parameters were calculated

for a data set covering a large range of total chlorophyll a

concentration (0.08–2.43 mg m-3). All three processors were

intercompared using 40 optical profiles; the JRC and GSFC

processors were further intercompared using an additional 10

casts (the larger data set increased the amount of data in very

clear waters, thereby extending the lowest total chlorophyll a

concentration to 0.027 mg m-3). The data were also separated

according to the oceanic environment, deep ocean (DO) or

shallow coastal (SC), and according to the instrument type,

free-fall (FF) profiler or winch and crane (WC) system. The

instruments used with these data included the SeaWiFS

Optical Profiling System (SeaOPS), the Low-Cost NASA

Environmental Sampling System (LoCNESS), the WireStabilized Profiling Environmental Radiometer (WiSPER),

and a variant of the miniature NASA Environmental Sampling

System (miniNESS). SeaOPS and WiSPER are deployed

using a winch and crane, whereas, LoCNESS and miniNESS

are floated away from the sampling platform and deployed by

hand. The deep ocean data were all from Case-1 conditions,

and the shallow coastal data were from Case-2 conditions or

from water near the threshold between Case-1 and Case-2 in

terms of the Loisel and Morel (1998) classification scheme.

In this study, no one data processing system was assumed

to be more correct than another, so the UPD was used for

intercomparisons. In terms of overall spectral averages, many

of the JRC and GSFC (JG) results intercompared to within

2.5%, but none of the Satlantic results intercompared with the

other processors (JS and GS) at this level. Band-ratio

averages, however, frequently intercompared to within 2.5%

for all processor combinations, even when the overall spectral

averages did not. If the JRC and GSFC processor options were

made more similar (same extrapolation intervals, data

filtering, etc.), the two processors usually intercompared to

within 1%. An example of this for the upwelled radiance at

null depth, Lu(0-

), and the diffuse attenuation coefficient, Kd, is

shown in Fig. 5. These variables were selected, because they

are very important to the calibration and validation process:

Lu(0-

) is used directly in calculating LW(λ), and Kd (Fig. 9.5b)

is the most common variable to describe the attenuation of

light in the upper layers of the water column.

0

5

10

15

20

25

JG JS GS JG JS GS JG JS GS JG JS GS JG JS GS

Instrument and Intercomparison

	
 
	
 

 
 



   

	
 
	
 

 
 



JG JS GS JG JS GS JG JS GS JG JS GS JG JS GS

Instrument and Intercomparison

0

5

10

15

20

25  !  





Figure 9.5: The average processing uncertainties for the JG,

JS, and GS intercomparisons of a) Lu(0-

), and b) Kd. The

dashed line sets the 2.5% intercomparison objective and the

dotted line the 1.0% objective. Uncertainties above 25% are

shown clipped at the 25% level, and the maximum

uncertainty achieved is given at the top of the panel to the

side of the clipped bars. The individual wavelengths are

shown as the sequential bars with varying intensities of gray

(going from left to right, blue is light gray and red is dark

gray). The instrument (and deployment location) codes are

given along the top of the subpanels. The overall average of

all the data (40 casts) is given in the right-most three

intercomparisons.
SIMBIOS Project Annual Report

69

The Lu(0-

) results (Fig. 9.5a) show the lowest

uncertainties with the Satlantic processor are for the shallow

coastal data, and in all cases, the largest uncertainties are for

the red wavelengths—none of the Satlantic uncertainties are

below 1.0%, although, the blue-green WiSPER wavelengths

are within 2.5%. Better agreement in the shallow coastal

(predominantly Case-2) data rather than the deep ocean (Case1) data are unexpected, but it has a simple explanation: the

shallow water depth constrains the options for selecting the

extrapolation interval.

The JG uncertainties are always below 2.5%, and mostly

below 1.0% except for the blue and red SeaOPS data. The Kd

uncertainties (Fig. 9.5b) show much higher overall

uncertainties with respect to the Satlantic processor,

particularly for the LoCNESS data set where every

wavelength has an uncertainty above 25%. None of the

Satlantic uncertainties are below 2.5%, whereas all of the JG

uncertainties are, and most of the JG uncertainties are to

within 1.0% (the primary exception are the WiSPER data).

These results suggest a database constructed with processed

data from a wide source of contributors will have substantially

higher uncertainties than a database constructed with raw data

which is processed with a single processor, although band

ratios regularly provide reduced uncertainties with respect to

individual spectral uncertainties.

To minimize observational uncertainties, the SeaWiFS

Project has sponsored a variety of multidisciplinary workshops

to outline the observations and sampling protocols required for

bio-optical algorithm development (Mueller and Austin 1992

and 1995). One of the consequences of the workshops was the

establishment of the series of aforementioned SIRREX

activities to demonstrate and advance the state of the art for

calibrating the instruments used in field activities. Although

prior SIRREX activities significantly reduced the uncertainties

in optical calibrations, some important sources of uncertainty

were not quantified. One of the most important of these, from

the vantage of the calibration equation, was the immersion

factor.2

For commercial radiometers, the raw data are converted

to physical units based on a formulation given by the

manufacturer. For Satlantic sensors, the ith sample for a

radiometer at center wavelength λ, are converted to physical

units according to the following equation:

 

C(λ,ti ) = Cc (λ)I f (λ) V(λ,ti [ ] ) − D(λ) (3)

 

2

 The immersion factor is the coefficient accounting for the

change in sensor responsivity when the in-air calibration is

applied to in-water measurements (If

 = 1 for above-water

sensors).

where C(λ,ti) is the calibrated value (C is replaced by Ed(z),

Eu(z), Lu(z), or Ed(0+

) depending on the sensor type), Cc(λ) is

the calibration coefficient, If(λ) is the immersion factor, V(λ,ti)

is the raw voltage (in digital counts) measured by the

instrument at time ti (which also sets the depth, z), and D(λ)

is the average dark value (in digital counts) measured during a

special “dark cast” with the caps on the radiometer.

The immersion factor is a first-order term in the sense that

the uncertainty in this term is represented at the same level as

the calibration coefficient, so the final uncertainty of the

calibrated data are heavily dependent on the uncertainty in the

immersion factor. For irradiance sensors, the immersion factor

is determined experimentally. A suggested and acceptable

procedure is as follows (Mueller and Austin 1995):

• The instrument is placed in a tank of water with all

surfaces painted black.

• The sensor is leveled with the irradiance collector plate

facing upward.

• A tungsten-halogen lamp with a small filament, powered

by a stable power supply, is placed at some distance

above the water surface.

• An initial reading is taken with the water level below the

collector, i.e., with the collector in the air and dry.

• The depth of the water is increased in steps and readings

are recorded for all wavelengths from each carefully

measured depth.

Note that in most cases, water is removed from the tank with a

pump, and data are usually recorded in between pumping

intervals.

The amount of energy arriving at the collector varies with

the water depth and is a function of several factors: a) the

attenuation at the air–water interface, which varies with

wavelength, b) the attenuation over the water pathlength,

which is a function of depth and wavelength; and c) the

change in solid angle of the light leaving the source and

arriving at the collector, caused by the light rays changing

direction at the air–water interface, which varies with

wavelength and water depth. When all of these effects are

properly accounted for, the immersion factor can be

computed.

The SIRREX-8 activity is taking place during the last

three months of 2001 and is concerned with the round-robin

determination of immersion factors for nine Satlantic

radiometers at three different calibration facilities (Hooker and

Zibordi 2002a). A picture of the tank used at the first facility,

the Center for Hydro-Optics and Remote Sensing (CHORS),

from 28 September to 9 October 2001 is shown in Fig. 9.6.

The other two facilities will be the JRC and Satlantic. All of

the data will be processed by the JRC to ensure there are no
SIMBIOS Project Annual Report

70

extra uncertainties as a result of differences in data processing

methods.

Fig. 9.6: The water tank used at the CHORS calibration

facility for measuring immersion factors. The radiometer is

set inside a large tube atop an aluminum grate that is

covered in a fine mesh. The mesh helps dissipate the

turbulence caused by the addition or removal of water.

A preliminary comparison of the immersion coefficients

for a sensor used during the first phase of SIRREX-8 at

CHORS is shown in Fig. 9.7 (Zibordi and Hooker 2002). For

the CHORS work, one radiometer, an Eu sensor (S/N 130),

was selected as a baseline instrument and was subjected to a

variety of experiments and trials. The data shown in Fig. 9.7

are for three of the trials when the amount of particles in the

tank was kept very low (clean water conditions ensure the

surface reflectance is in keeping with the Fresnel reflectance

assumption).

The CHORS data show excellent agreement between

trials and good agreement with respect to the average value

from the three days of data. The latter is an important point,

because the a priori expectation is that the immersion

coefficients should be spectrally uniform (within

experimental, manufacturing, and age-dependent limits). The

CHORS data shows this is basically the case, except in the

blue where the values at 412 nm are depressed and the values

at 443 nm are elevated. The Satlantic values show a much

greater spectral dependence with maximal values in the blue,

decreasing through the green, and then minimal in the red. The

differences between the Satlantic and CHORS data are

significantly large, i.e., they are much larger than the 2%

uncertainty demonstrated for Satlantic irradiance calibrations

during SIRREX-7 (Hooker et al. 2001b). The immersion

coefficients can be presented as percentages, so the maximum

differences in the blue, green, and red are approximately 19, 8,

and 6%, respectively, for these initial results.

Most manufacturers, Satlantic included, do not

characterize the immersion coefficients for each instrument

they produce, because of the time (and thus, cost) involved in

the extra laboratory work. The standard technique, as

described above, can require as much as—or even more

than—2 h to complete one realization. Assuming three trials

are an acceptable minimum for characterization work, this

means almost one day of laboratory time would have to be

added to the cost of each instrument. Consequently,

instrument manufacturers rely on characterizing a class of

diffuser designs and then assigning the immersion factors to

all instruments manufactured within the class.

The SIRREX-7 preliminary results suggest the class

characterization approach is questionable and a considerable

amount of effort is needed to recharacterize the instruments

being used in calibration and validation activities. In an effort

to lower the cost involved, one of the objectives of SIRREX-7

was to test new procedures that can be executed in less time

than the standard method (approximately one-sixth the time),

or executed with lower cost components (Hooker and Zibordi

2002b).

Figure 9.7: The immersion coefficients supplied by

Satlantic (filled circles) and as determined with the JRC

data processor from the CHORS data during SIRREX-8 for

Eu sensor S/N 130 for three days of measurements (open

circles). The dashed line is the average from the three

CHORS measurements.
SIMBIOS Project Annual Report

71

9.4 FUTURE WORK

The planned activities (and nominal schedule) for the second

year (FY02) are as follows:

• Complete the laboratory trials and the publications

associated with the immersion coefficient study;

• Analyze and document the importance of tower

perturbations on above-water measurements from an

offshore structure;

• Estimate the uncertainties in Labsphere plaque calibration

factors (they do not provide a directional-directional

calibration that is the same as the way the plaques are

used in radiometric calibrations);

• Validate the second-generation microNESS profiler

(which will now have the capability of measuring Eu ) and

the second-generation microSAS above-water instrument

(February–March 2002);

• Ascertain why the theoretical and in situ Q-factors do not

agree;

• Reprocess the SeaWiFS Field Team data set with

corrected immersion coefficients (a new data processor is

in the final stages of testing); and

• Continue field campaigns for ocean color calibration and

validation including a Medium Resolution Imaging

Spectrometer (MERIS) cruise within 120 days of the start

of operations (April–May 2002), initialization of the

SeaPRISM network (June 2002), and site characterization

experiments for the Bouée pour l’acquisition de Séries

Optiques à Long Terme3

 (BOUSSOLE) Program (August

2001).

For the Q-factor work, the new GSFC optical data

processor is being modified to explore particular aspects of

instrument problems with respect to calculating the Q-factor

from in situ data.

REFERENCES

Aiken, J., N.W. Rees, S. Hooker, P. Holligan, A. Bale, D.

Robins, G. Moore, R. Harris, and D. Pilgrim, 2000: The

Atlantic Meridional Transect: overview and synthesis of

data. Prog. Oceanogr., 45, 257–312.

Austin, R.W., 1974: “The Remote Sensing of Spectral

Radiance from Below the Ocean Surface.” In: Optical

Aspects of Oceanography, N.G. Jerlov and E.S. Nielsen,

Eds., Academic Press, London, 317–344.

 

3

 Buoy for the acquisition of a long-term optical series.

Firestone, E.R., and S.B. Hooker, 2001: SeaWiFS Postlaunch

Technical Report Series Cumulative Index: Volumes 1–

11. NASA Tech. Memo. 2001–206892, Vol. 12, S.B.

Hooker and E.R. Firestone, Eds., NASA Goddard Space

Flight Center, Greenbelt, Maryland, 24 pp.

Hooker, S.B., and W.E. Esaias, 1993: An overview of the

SeaWiFS project. Eos, Trans., Amer. Geophys. Union, 74,

241–246.

—, —, and L.A. Rexrode, 1993: Proceedings of the First

SeaWiFS Science Team Meeting. NASA Tech. Memo.

104566, Vol. 8, S.B. Hooker and E.R. Firestone, Eds.,

NASA Goddard Space Flight Center, Greenbelt,

Maryland, 61 pp.

—, C.R. McClain, J.K. Firestone, T.L. Westphal, E-n. Yeh,

and Y. Ge, 1994: The SeaWiFS Bio-Optical Archive and

Storage System (SeaBASS), Part 1. NASA Tech. Memo.

104566, Vol. 20, S.B. Hooker and E.R. Firestone, Eds.,

NASA Goddard Space Flight Center, Greenbelt,

Maryland, 40 pp.

—, and J. Aiken, 1998: Calibration evaluation and radiometric

testing of field radiometers with the SeaWiFS Quality

Monitor (SQM). J. Atmos. Ocean. Tech., 15, 995–1,007.

—, and S. Maritorena, 2000: An evaluation of oceanographic

radiometers and deployment methodologies. J. Atmos.

Ocean. Tech., 17, 811–830.

—, and C.R. McClain, 2000: The Calibration and Validation

of SeaWiFS Data. Prog. Oceanogr., 45, 427–465.

—, G. Zibordi, J-F. Berthon, S.W. Bailey, and C.M. Pietras,

2000: The SeaWiFS Photometer Revision for Incident

Surface Measurement (SeaPRISM) Field Commissioning.

NASA Tech. Memo. 2000–206892, Vol. 13, S.B. Hooker

and E.R. Firestone, Eds., NASA Goddard Space Flight

Center, Greenbelt, Maryland, 24 pp.

—, and A. Morel, 2001: Platform and environmental effects

on above- and in-water determinations of water-leaving

radiances. J. Atmos. Ocean. Tech., (submitted).

—, S. Maritorena, G. Zibordi, and S. McLean, 2001a: Results

of the Second SeaWiFS Data Analysis Round-Robin,

March 2000 (DARR-00). NASA Tech. Memo. 2001–

206892, Vol. 15, S.B. Hooker and E.R. Firestone, Eds.,

NASA Goddard Space Flight Center, Greenbelt,

Maryland, 71 pp.
SIMBIOS Project Annual Report

72

—, S. McLean, J. Sherman, M. Small, G. Zibordi, and J.

Brown, 2001b: The Seventh SeaWiFS Intercalibration

Round-Robin Experiment (SIRREX-7), March 1999.

NASA Tech. Memo. 2001–206892, Vol. 17, S.B. Hooker

and E.R. Firestone, Eds., NASA Goddard Space Flight

Center, Greenbelt, Maryland, (in press).

—, and G. Zibordi, 2002a: The Eighth SeaWiFS

Intercalibration Round-Robin Experiment (SIRREX-8),

September–December 2001. NASA Tech. Memo. 2002–

206892, Vol. 19, S.B. Hooker and E.R. Firestone, Eds.,

NASA Goddard Space Flight Center, Greenbelt,

Maryland, (in prep.).

—, and —, 2002b: New methods for determining the

immersion coefficient of marine radiometers. Limnol.

Oceanogr., (in prep.).

Joint Global Ocean Flux Study, 1991: JGOFS Core

Measurements Protocols. JGOFS Report No. 6, Scientific

Committee on Oceanic Research, 40 pp.

Loisel, H., and A. Morel, 1998: Light scattering and

chlorophyll concentration in case 1 waters: A

reexamination. Limnol. Oceanogr., 43, 847–858.

Mobley, C.D., 1999: Estimation of the remote-sensing

reflectance from above-surface measurements. Appl. Opt.,

38, 7,442–7,455.

Morel, A., 1980: In-water and remote measurements of ocean

color. Bound.-Layer Meteorol., 18, 177–201.

—, and B. Gentili, 1996: Diffuse reflectance of oceanic

waters, III. Implication of bidirectionality for the remote

sensing problem. Appl. Opt., 35, 4,850–4,862.

Mueller, J.L., 2000: “Overview of measurement and data

analysis protocols” and “In-water radiometric profile

measurements and data analysis protocols.” In: Fargion,

G.S., and J.L. Mueller, Ocean Optics Protocols for

Satellite Ocean Color Sensor Validation, Revision 2.

NASA Tech. Memo. 2000–209966, NASA Goddard Space

Flight Center, Greenbelt, Maryland, 87–97.

—, and —, 1992: Ocean Optics Protocols for SeaWiFS

Validation. NASA Tech. Memo. 104566, Vol. 5, S.B.

Hooker and E.R. Firestone, Eds., NASA Goddard Space

Flight Center, Greenbelt, Maryland, 43 pp.

—, and —, 1995: Ocean Optics Protocols for SeaWiFS

Validation, Revision 1. NASA Tech. Memo. 104566, Vol.

25, S.B. Hooker, E.R. Firestone, and J.G. Acker, Eds.,

NASA Goddard Space Flight Center, Greenbelt,

Maryland, 66 pp.

—, B.C. Johnson, C.L. Cromer, S.B. Hooker, J.T. McLean,

and S.F. Biggar, 1996: The Third SeaWiFS

Intercalibration Round-Robin Experiment, SIRREX-3,

September 1994. NASA Tech. Memo. 104566, Vol. 34,

S.B. Hooker, E.R. Firestone, and J.G. Acker, Eds., NASA

Goddard Space Flight Center, Greenbelt, Maryland, 78

pp.

Siegel, D.A., M.C. O’Brien, J.C. Sorensen, D.A. Konnoff,

E.A. Brody, J.L. Mueller, C.O. Davis, W.J. Rhea, and

S.B. Hooker, 1995: Results of the SeaWiFS Data

Analysis Round-Robin (DARR-94), July 1994. NASA

Tech. Memo. 104566, Vol. 26, S.B. Hooker and E.R.

Firestone, Eds., NASA Goddard Space Flight Center,

Greenbelt, Maryland, 58 pp.

Zibordi, G., J.P. Doyle, and S.B. Hooker, 1999: Offshore

tower shading effects on in-water optical measurements.

J. Atmos. Ocean. Tech., 16, 1,767–1,779.

—, S.B. Hooker, J-F. Berthon, and D. D’Alimonte, 2002:

Autonomous above-water radiance measurements from an

offshore platform: A field assessment experiment. J.

Atmos. Ocean. Tech., (in press).

—, and —, 2002: An evaluation of immersion coefficients for

Satlantic marine radiometers. J. Atmos. Ocean. Tech., (in

prep.).
SIMBIOS Project Annual Report

73

Chapter 10

Optimization Of Ocean Color Algorithms: Application To Satellite

And In Situ Data Merging.

Stéphane Maritorena and David A. Siegel

Institute for Computational Earth System Science, UCSB, Santa Barbara, CA 93106-3060, USA

André Morel

Laboratoire de Physique et Chimie Marines, Villefranche Sur Mer, France

10.1 INTRODUCTION

The objective of our program is to develop and validate a

procedure for ocean color data merging which is one of the

major goals of the SIMBIOS project. While ocean color data

merging is generally intended to operate using NASA's Level3 global gridded products (IOCCG, 1999), our merging

procedure uses a semi-analytical ocean color model and takes

place at the normalized water-leaving radiance level (Level-2).

There are several benefits to such an approach: it is consistent

in terms of the algorithm used to generate data products (along

with their associated uncertainties), it generates not only Chl

but also additional inherent optical properties (IOP), it can

handle data sources with similar or different bands and,

uncertainties in the data sources can be accounted for in the

merging procedure. The core of the procedure is based on the

semi-analytical ocean color model described by Garver and

Siegel (1987) and updated by Maritorena et al. (2001),

thereafter referred to as the GSM01 model. The procedure has

been tested successfully with in situ data (Siegel &

Maritorena, 2001). Although the model has been optimized for

global scale applications it is still tentative because the data set

used for its optimization was not fully suited for that purpose

and because the formulation of several sub-components of the

model can be improved. To overcome the first of these

limitations we are developing a comprehensive ocean color

data set that contains all the inherent and apparent optical

properties required for ocean color semi-analytical algorithm

development and testing. Once the data set will be completed,

it will be used to develop the final version of the merging

algorithm. In addition, a new optimization of the model

parameters (i.e. the coefficients used in the model subcomponents) is foreseen to relax some simplifying

assumptions used in the current model. The use of the merging

procedure with satellite data will also require a BRDF

correction scheme to take the different viewing and

illumination geometry of the data sources into account. Global

and regional optimized parameterizations will be compared

and ways to reconcile the two approaches will be investigated.

10.2 RESEARCH ACTIVITIES

Most of the work since the beginning of the SIMBIOS

contract has been devoted to the development of the IOP/AOP

data set. The original list of required parameters and

information is listed in Table 10.1. Most of the data included

in the present data set come from the NASA SIMBIOS

SeaBASS archive although some data sets or subsets were

directly provided to us. We have collected all available

wavelengths for each of the variables listed in table to ensure

analyses can be conducted with any individuals or

combinations of bands. The current status of the data set is

presented on Table 10.2 as well as the number of wavelengths

available for each variable. Note that a "NA" in Table 10.2

does not necessarily mean that the data does not exist, it may

indicate that they are not yet incorporated in the data set.

Several quality control (QC) procedures have been developed,

including :

• Test for negative values (except the default "missing"

value).

• Comparison of Kd values with Kd for pure seawater

(lower limit)

• Comparison of Kd values with Kd modeled as in

Morel & Maritorena, (2001)

• Comparison of Rrs data [= Lu(0+)/Ed(0+) = Lw/Es]

with the SeaBAM data.

• Comparison of Rrs data with Rrs modeled as in

Morel & Maritorena, (2001)
SIMBIOS Project Annual Report

74

• Comparison of Ed(0-) and Es (=Ed(0+)

• Comparison of Ed(0+) with the mean solar

extraterrestrial irradiance (upper limit)

• Comparison of ap and aph data with the Morel and

Maritorena, (2001) model (when Chl is available)

• Check value of aph*(675)

• Computation of the slope, S, of ag(λ) and ad(λ).

• Comparison of bb data to various bb models

(Kopelevich, 1983, Gordon et al., 1988, Morel and

Maritorena, 2001 when Chl is available or Carder et

al., 1999 when Rrs is available).

Calculate exponent of particulate backscattering (n)

Several analyses have been conducted on the data. In

particular, the component absorption has been studied using

the data collected in the AOP/IOP data set and this studies

demonstrates that the absorption of the soluble fraction of

seawater is a major contributor to the absorption budget over

most of the Chl range of oceanic waters. These data were also

used to validate the acdm retrievals of the GSM01 model

derived from SeaWiFS data (Fig. 9.1, Siegel et al., 2001).

As mentioned earlier, we are in the process of improving

the GSM01 model. The main feature we are working on is a

parameterization that allows the model to work with data

sources having any kind of bands ensuring a full compatibility

of the model whatever the satellite being used in the merging

process. The alternative parameterizations for particulate

backscattering (bbp) are also being tested.

Since we plan on first testing the merging procedure using

SeaWiFS and MODIS data, we have downloaded some

MODIS Level-2 data from the GES DAAC in order for us to

get more familiar with the data. This work is underway.

10.3 FUTURE WORK

We will continue to develop and QC the AOP/IOP data

set. The first tests of satellite data merging using our

procedure will take place during the second year of our

SIMBIOS contract. We plan on merging SeaWiFS and

MODIS data for a few case-studies sites were in situ data are

generally available (e.g. BBOP, CalCOFI, Plumes and Blooms

or any other " SIMBIOS Diagnostic site"). Beside the data

merging itself, one key aspect of these initial tests will be to

configure properly the weighting of the data sources based on

their associated incertainties (see Siegel and Maritorna, 2001).

We will implement new parameterizations in the GSM01

model and optimize its parameters using the AOP/IOP data set

and simulated annealing (Maritorena et al., 2001).

REFERENCES

Carder K.L., F.R. Chen, Z.P. Lee, S.K. Hawes and D.

Kamykowski. 1999: Semianalytic Moderate-Resolution

Imaging Spectrometer algorithms for chlorophyll a and

absorption with bio-optical domains based on nitratedepletion temperatures. J. Geophys. Res., 104: (C3)

5403-5421.

Garver, S.A., and D.A. Siegel, 1997: Inherent optical property

inversion of ocean color spectra and its biogeochemical

interpretation: I. Time series from the Sargasso Sea. J.

Geophys. Res., 102, 18,607-18,625.

Gordon, H.R., O.B. Brown, R.H. Evans, J.W. Brown, R.C.

Smith, K.S. Baker, and D.K. Clark. 1988: A semianalytic radiance model of ocean color. J. Geophys.

Res., 93, 10,909-10,924.

Kopelevitch, O.V. 1983: Small-parameter model of optical

properties of sea water, Chapter 8 in Ocean Optics, vol

1: Physical Ocean Optics, A.S. Monin (Ed.), Nauka

Pub., Moscow.

Maritorena S., D.A. Siegel and A. Peterson. 2001:

Optimization of a Semi-Analytical Ocean Color Model

for Global Scale Applications. In revision for Applied

Optics.

Morel A. and S. Maritorena. 2001: Bio-optical properties of

oceanic waters : a reappraisal. J. Geophys. Res.,

106(C4) : 7163-7180.

Siegel, D.A. and S. Maritorena, 2001: Spectral Data

assimilation for merging satellite Ocean Color imagery.

SIMBIOS Project 2000 Annual Report. NASA TM2001-209976. G. Fargion & C. R. McClain Eds, NASA

Goddard Space Flight Center, Greenbelt, Maryland, pp

117-123.

Siegel, D.A., S. Maritorena, N.B. Nelson, D.A. Hansell and

M. Lorenzi-Kayser. 2001: Global Distribution and

Dynamics of Colored Dissolved and Detrital Organic

Materials. In Press Journal of Geophysical Research.
SIMBIOS Project Annual Report

75

This research was supported by

the SIMBIOS NASA contract #00196

PEER REVIWED PUBLICATIONS

Maritorena S., D.A. Siegel & A. Peterson. 2001: Optimization

of a Semi-Analytical Ocean Color Model for Global Scale

Applications. In revision for Applied Optics.

Siegel, D.A., S. Maritorena, N.B. Nelson, D.A. Hansell & M.

Lorenzi-Kayser. 2001: Global Distribution and Dynamics

of Colored Dissolved and Detrital Organic Materials. In

Press Journal of Geophysical Research.

Morel A. and S. Maritorena. 2001: Bio-optical properties of

oceanic waters : a reappraisal. J. Geophys. Res., 106(C4) :

7163-7180.

PRESENTATIONS

O'Reilly J. and S. Maritorena 2001: A Review of Bio-Optical

Chlorophyll Algorithms and Future Challenges.

Presentation Fall AGU Meeting, San Francisco, Dec.

2001.

Maritorena S, D.A. Siegel 2001: Dominance of Colored

Dissolved Organic Material in Determining Light

Availability in the Sea. Poster Fall AGU Meeting, San

Francisco, Dec. 2001.

Figure 10.1: Comparison of near-simultaneous SeaWiFS and in situ CDM observations from around the world (N = 523). The

r

2

 value for the log-transformed CDM estimates is 63.42% and the fit power-law relation is CDMWiFS = 0.775 * (CDMfield)

0.935

(the middle dotted line). The outer dotted lines represent 95% confidence interval for the fit.(From Siegel et al., 2001).
SIMBIOS Project Annual Report

76

Table 10.1: Required observations for the AOP-IOP data set

Data type Description Notation

AOP Spectral upwelled radiance below the surface Lu(λ, 0-)

Spectral upwelled irradiance below the surface Eu(λ, 0-)

Spectral incident irradiance at the surface Ed(λ, 0+)

Spectral diffuse attenuation coefficient Kd(λ)

IOP Spectral total absorption coefficent a(λ)

Spectral absorption coefficient of phytoplankton aph(λ)

Spectral absorption coefficient of detrital particulate ad(λ)

Spectral absorption coefficient of dissolved organic matter ay(λ)

Spectral backscattering coefficient of particulate bbp(λ)

Spectral beam attenuation coefficient c(λ)

Pigment data HPLC pigments

Fluorometric [Chl a]

Biliproteins

Metadata Date of collection of the data

Time of collection of the data

Latitude and longitude

Description of sea state

Description of sky state

Instrument identification

Calibration information

Cruise identification

Station identification

Table 10.2: Present status of the AOP/IOP data set (extract). The first number in each cell indicates the number

 of stations for which data are available. The numbers in parentheses indicate the number of available wavelengths.

Experiment Chl Kd Lw Es bb ad ag aph

Indoex 35 35 (12) 35 (6) 35 (12) na 35 (250) na na

Ace-Asia 50 50 (18) 50 (18) 50 (18) na 50 (225) 50 (200) 50 (225)

BBOP 142 142 (12) 142 (12) 142 (6) na 86 (250) 86 (200) 86 (250)

CALCOFI 176 160 (12) 160 (12) 97 (12) na 273 (225) 273 (150) 273 (225)

ECOFRONT 16 na 28 (6) 28 (6) na 16 (200) 16 (148) 16 (195)

EcoHAB 180 80 (1) 80 (6) 80 (6) na 180 (200) 180 (150) 180 (200)

Globec

Biomapper 26 na na na na 26 (250) 26 (250) 26 (250)

GOCAL na na 54 (7) 52 (5) 41 (6) na na na

Gulf of Maine 148 na 55 (7) 55 (7) na 148 (200) 148 (209) 148 (150)

JGOFS 68 68 (12) 68 (12) 68 (12) na 68 (225) 68 (225) 68 (225)

Ocean Research

Consortium 25 na 25 (6) 25 (5) 25 (3) 21 (470) 21 (470) 21 (470)

Plumes and

Blooms 370 304 (7) 304 (9) 304 (12) 91 (7) 338 (225) 338 (225) 338 (225)

Sea of Japan 36 36 (12) 36 (12) 36 (12) 36 (6) na na na

Scotia Prince

Ferry

4442 na 2191 (6) 4009 (6) 4433 (4) na na na

Tongue of the

Ocean 96 na 82 (6) 82 (6) 16 (6) 96 (200) 96 (225) 96 (200)

Total 5810 875 2466 5063 4642 1337 1302 1302
SIMBIOS Project Annual Report

77

Chapter 11

Aerosol Optical Properties over the Oceans: A Summary of the

FRSR Database, Calibration Protocol and Measurement

Uncertainties

Mark A. Miller, R.M Reynolds, and M.J. Bartholomew

The Brookhaven National Laboratory, Earth Systems Science Division, New York

11.1 INTRODUCTION

The aerosol scattering component of the total radiance

measured at the detectors of ocean color satellites is

determined with atmospheric correction algorithms. These

algorithms are based on aerosol optical thickness

measurements made in two channels that lie in the nearinfrared portion of the electromagnetic spectrum. The aerosol

properties in the near-infrared region are used because there is

no significant contribution to the satellite-measured radiance

from the underlying ocean surface in that spectral region. In

the visible wavelength bands, the spectrum of radiation

scattered from the turbid atmosphere is convolved with the

spectrum of radiation scattered from the surface layers of the

ocean. The radiance contribution made by aerosols in the

visible bands is determined from the near-infrared

measurements through the use of aerosol models and radiation

transfer codes. Selection of appropriate aerosol models from

the near-infrared measurements is a fundamental issue.

There are several challenges with respect to the

development, improvement, and evaluation of satellite oceancolor atmospheric correction algorithms. A common thread

among these challenges is the lack of over-ocean aerosol data.

Until recently, one of the most important limitations has been

the lack of techniques and instruments to make aerosol

measurements at sea. There has been steady progress in this

area over the past five years, and there are several new and

promising devices and techniques for data collection. The

development of new instruments and the collection of more

aerosol data from over the world’s oceans have brought the

realization that aerosol measurements that can be directly

compared with aerosol measurements from ocean color

satellite measurements are difficult to obtain. There are two

problems that limit these types of comparisons: the cloudiness

of the atmosphere over the world’s oceans and the limitations

of the techniques and instruments used to collect aerosol data

from ships. To address the latter, we have developed a new

type of shipboard sun photometer.

There are two primary types of sun photometer used to

measure the aerosol optical depth: narrow field-of-view

radiometers aimed at the solar disk and rotating shadow-band

radiometers. Shadow-band radiometers are wide-field-ofview radiometers that employ an occulting apparatus to add

directional capabilities. Just as narrow-field-of-view

radiometers must be accurately aimed, a serious drawback for

measurements on ships at sea, conventional shadow-band

radiometers also require exact orientation. Fast-Rotating

Shadow-band Radiometers (FRSRs) are a hybrid form of the

original shadow-band radiometer design, but do not have a

rigid requirement for orientation. Thus, it is possible to use a

slightly modified version of the land-based FRSR on ships.

As compared to land-based FRSRs, shipboard FRSRs require

much faster rotation of the occulting arm, faster-response

silicon detectors, higher data sampling rates, and more

sophisticated data analysis.

As part of our NASA SIMBIOS contract and with

additional support from the Department of Energy’s (DOE)

Atmospheric Radiation Measurement (ARM) Program, we

developed and deployed a new instrument during the past two

years: the shipboard Fast-Rotating Shadow-band Radiometer

(FRSR; Reynolds et al., 1999). This instrument makes

continuous, semi-automated shipboard measurements of the

direct-normal, diffuse, and global irradiance in seven channels

(415 nm, 500 nm, ~610 nm, ~660 nm, ~862 nm, 936 nm, and

broadband) and does not require a mechanically stabilized

platform, thereby making it cost effective and reliable. The

FRSR can operate continuously for months on a ship and can

be monitored by someone with minimal training. The FRSR

is part of a Portable Radiation Package (PRP), which also

contains broadband solar and IR Eppley radiometers. The

PRP has been deployed extensively during the past two years,

traversing parts of all three oceans, and has demonstrated

stable calibration. It has moved from proof-of-concept to a

nearly operational instrument.

The shipboard FRSR measures spectral global and diffuse

irradiance with 2-minute resolution. These data are combined

with information about the pitch, roll, and heading of the ship

to calculate the 2-minute spectral direct-normal irradiance.

From these data, the aerosol optical thickness can be

computed during clear periods using the Langley regression

technique, or continuously if high quality extraterrestrial

calibration coefficients are known. In addition, the diffuse

irradiance measured by the FRSR can be used to evaluate
SIMBIOS Project Annual Report

78

fractional cloudiness, thereby providing a means to test cloudfiltering algorithms used in satellite ocean color retrievals.

Our strategy for collecting relevant aerosol data from over

the oceans has evolved over the past four years. As a

benchmark of our progress, we (our group at the Brookhaven

National Laboratory) believe that we have increased the

number of shipboard aerosol measurements over the world’s

oceans by several orders of magnitude. In this report, we

summarize our progress in expanding the data base of over

ocean optical measurements during the past year. We also

discuss our data processing strategy, especially our uncertainty

analyses, and comment on plans for the future.

Evaluating Ocean Color Atmospheric Correction Algorithms

An aerosol correction algorithm was developed by

Gordon (1978), applied to the problem of phytoplankton

pigment retrieval by Gordon and Clark (1981), and used in the

Coastal Zone Color Scanner by Gordon et al. (1980).

Mathematically, the atmospheric correction parameters,

(, ) i j ελλ , for the Gordon scheme are given by

 0

0

( ) ( ) (, , ) (, ) , ( ) ( ) (, , )

aia i a i

i j

a ja j a j

p

p

ω λτ λ θθ λ ελλ ω λ τ λ θθ λ = (1)

where ( ) ω λ a i is the aerosol single scattering albedo,

( ) a τ λ is the aerosol optical thickness, and 0 (, , )i p θθ λ is the

aerosol scattering phase function. The Gordon technique is to

model (, ) i j ελλ using a power-law relationship defined as

 (, ) .

n

i

i j

j

λ ελλ λ

−

 

=      

 (2)

The coefficient n is determined for two wavelengths in

the near infrared and used to select an aerosol model. From

this model, radiation transfer codes are used to estimate the

satellite radiance contribution of the aerosol in the visible

wavelengths (Gordon and Wang, 1994).

Evaluation of this atmospheric correction scheme

therefore involves collection of shipboard data from which the

characteristics of (, ) i j ελλ may be determined for a wide

variety of aerosol conditions. The majority of the variance

associated with (, ) i j ελλ is the result of variance in λa τ , so

measurements of λa τ provide a surrogate for explicit

measurements of (, ) i j ελλ .

11.2 REASERCH RESULTS

There are currently nine PRPs with their resident FRSRs;

a tenth PRP is under construction. As shown in Table 10.1,

four of the ten instruments are permanently deployed on

vessels that traverse various parts of the world’s oceans:

National Oceanic and Atmospheric Administration’s (NOAA),

NOAA’s Ronald Brown, Japan Marine Science Center’s R/V

Mirai, and Royal Caribbean Cruise Line’s Explorer of the

Seas. Four of the five remaining units are deployed on ships

of opportunity, three in support of University of Miami

projects. They are deployed on medium to large ships making

lengthy voyages or on ships traveling to a unique part of the

world’s oceans. Data are collected continuously from the

instrument using a laptop computer, recorded on CD or disk,

and shipped to BNL for analysis. Table 10.2 shows a

catalogue of the data that currently reside in the database. The

initial releases of most of these data have been submitted to

SEABASS.

Calibration Of The Frsr

Calibration of the FRSRs is done at regular intervals (six

months to one year) at a high elevation location using the

Langley Method. In the past, the traditional location for these

calibrations has been Mauna Loa. Due to the costs associated

with Mauna Loa deployments, last spring we attempted a

calibration of our radiometers at the astronomical observatory

on Mt. Lemmon in Arizona. The Mt. Lemmon site is at

considerable elevation (3 km) and is located in the dry desert

southwest. Analyses of the Mt. Lemmon calibration data

show that it is a worthy site for calibration in the late spring

after most of the snow on the mountain has melted. Summer

may be a less optimal time for calibration at Mt. Lemmon due

to the increasing frequency of convection above the peaks.

Analyses also show that the morning Langley plots should be

used, as upslope wind conditions in the afternoon tend to

transport aerosols from the boundary layer into the atmosphere

above the radiometer site on Mt. Lemmon. In summary, Mt.

Lemmon appears to be a cost-effective solution for the

calibration of radiometers for the SIMBIOS program.

Another important finding during the past year involves

the capability to perform spot Langley calibrations on the

ships on clear days. The section that follows details work to

quantify the uncertainties associated with measurements of

aλ τ and α made with the FRSR on ships. These calculations

demonstrate the difficulty associated with Langley calibrations

at sea. The crux of the problem is that the uncertainties

associated with the measurement of direct-normal irradiance

on a ship increase as the solar zenith angle increases. The

Langley calibration technique is strongly dependent on the

measurements made at high zenith angles, so calibration of

marine FRSRs must be made on land because shipboard
SIMBIOS Project Annual Report

79

0 20 40 60 80

0

0.02

0.04

0.06 λ=415; τ=0.05; α=1.5

σIo=5%

σIo=2%

θr

στ

(a)

0 20 40 60 80

0

0.02

0.04

0.06 λ=500; τ=0.05; α=1.5

σIo=5%

σIo=2%

θr

στ

(b)

0 20 40 60 80

0

0.02

0.04

0.06 λ=610; τ=0.05; α=1.5

σIo=5%

σIo=2%

θr

στ

(c)

0 20 40 60 80

0

0.02

0.04

0.06 λ=660; τ=0.05; α=1.5

σIo=5%

σIo=2%

θr

στ

(d)

0 20 40 60 80

0

0.02

0.04

0.06 λ=860; τ=0.05; α=1.5

σIo=5%

σIo=2%

θr

στ

(e)

Figure 11.1: Plots of the uncertainty in the measurement of λa τ , λa στ , versus the solar zenith angle,θr , for a clean maritime

airmass with large sea-salt particles( 0.05 λa τ = , α = 0) at (a) 415 nm, (b) 500 nm, (c) 610 nm, (d) 660 nm, and (e) 860

nm. The worst case scenario for the relationship between the solar azimuth and ship azimuth combined with the best case for

calibration of the extra-terrestrial irradiance (2% uncertainty) is shown for a ±5

 envelope of ship pitch angles (the solid line

within the envelope is zero degrees). The best case scenario is indicated with the solid line that moves from 0.02 to

progressively lower values as the solar zenith angle increases. The dashed line above shows the approximate displacement of

the family of curves if the calibration uncertainty is increased to 5%.

Table 11.1: Current Deployments

Unit Ship or Location Status Location

P01 Akademic Ioffe Ship of Opportunity Atlantic transect and Antarctic

P02 Ka’imimoana Permanent Tropical Western Pacific

P03 Ewing Ship of Opportunity Piraeus to Freemantle

P04 BNL Ship of Opportunity BNL Radiation Platform

P05 Explorer of the Seas Permanent Caribbean

P06 JAMSTEC Preparing for Island Deploy Tropical Western Pacific

P07 Mirai Permanent North Pacific to Tropics

P08 BNL Transfer Standard BNL Radiation Platform

P09 Ron Brown Permanent Variable

P10 Under Construction Ship-of-opportunity To be determined
SIMBIOS Project Annual Report

80

03/18 03/25 04/01 04/08

0

0.5

1

1.5

date

τ500

03/18 03/25 04/01 04/08

0

0.5

1

1.5

date

τ500

Error Bars Indicate Measurement Uncertainties

Aerosol Optical Thickness with Uncertainty Less Than 0.03 

Figure 11.2: (a) Plots of λa τ at 500 nm for the ACE-ASIA Experiment, along with the calculated uncertainty of each

individual measurement as a consequence of ship motion. (b) plots of λa τ at 500 nm for which the uncertainty in the

measurement of λa τ is 0.03 or less.

calibrations contain too much inherent uncertainty due to

platform motion.

We have amassed a data record that is long enough for us

to address calibration drift in the FRSR head. There are two

methods that can be used to quantify this drift. The most

obvious method is to compare the values of extra-terrestrial

irradiance between two Langley calibrations, which are

typically performed a few months apart. This method will

reveal drift, but gives no information about the temporal

variability of the drift between the two calibrations. A method

that we are currently developing relies on comparison of the

broadband irradiance measured by the FRSR with the

broadband irradiance measured with the independent Eppley

PSP, which is part of the portable radiation package. This

technique has the advantage of providing a time history of the

drift, since the calibration of the Eppley PSP is more stable.

We have used this technique to examine calibration drift on

the FRSR and found that it does not always decay in a steady

manner during deployments. The calibration of the FRSR on

some of our units tends to change incrementally, while little or

no drift is observed in other units.

We theorize that the calibration drift we are observing is

caused by degradation of the material used in the head diffuser

assembly. Using the PSP, we can correct for this drift, but we

are likely to institute a routine swap of the diffusers in our

units on a periodic basis.
SIMBIOS Project Annual Report

81

Frsr Data Analysis

We have continued to aggressively pursue a method to

quantify the uncertainties associated with our measurements.

During the past year, we have successfully completed this task

and are currently applying it to our database. The foundation

of our method is a calculation of the variance, 2 σ , associated

with the measurement techniques. The variance equations for

( ) a τ λ and α (the Angstrom exponent), are given by

0

2 2 2

2

0

1 N

R O

I I

mI I N

λ λ

λ λλ τ ττ

λ λ

σ σ

σ σσ

     = + ++              

 (3)

1 2

1/2 2 2 2

2 1

1 2

ln λ λ τ τ

α

λ λ

λ σ σ

σ λττ

−

       = +                  

, (4)

where m is the air mass and Iλ is the direct-normal

irradiance. The subscripts 0 and N indicate the extraterrestrial value and the measured value, respectively, and the

subscripts 1 and 2 indicate the two wavelengths used in the

calculation of the Angstrom exponent. The subscripts R and

O denote the Rayleigh and Ozone contributions. These

variance equations provide a means to evaluate 2 σ for each

individual measurement, thereby permitting a running

assessment of the quality of the aerosol measurements. The

variance equations also permit the data to be filtered according

to the uncertainty in ( ) a τ λ , α , or both variables.

The challenge in using (3) and (4) is the definition of

0

2

Iλ σ and 2

IλN σ , the variances in the extra-terrestrial

irradiance and the direct-normal irradiance. The former can

be evaluated by comparing Langley calibration derived values

of 0 Iλ with those measured by a combination of satellite and

model calculations (see Thullier et al., 1988). These

comparisons generally indicate that our absolute calibrations

are within 2% of the accepted values. The term 2

N Iλ σ is more

challenging. Expansion of this term reveals that it is

dependent on the uncertainty in the orientation of the

radiometer with respect to the sun. To demonstrate and

quantify this uncertainty, we have developed a full model of

the functional dependence of 2

N Iλ σ on radiometer orientation,

which is in turn a function of the wave conditions and sun

angle. Results from this model (Figure 11.1) show that the

uncertainty in a measurement of aλ τ is dependent on the

orientation of the radiometer with respect to the sun. The

uncertainty in aλ τ , στ , can be less than 0.02 for all solar

zenith angles if the relative solar azimuth angle is orthogonal

to the pitch and/or roll plane of the ship. If the pitch and/or

roll is in the same plane as the relative solar azimuth angle,

στ rapidly degrades and can exceed 0.04 at large relative

solar zenith angles. Of critical importance, however, is

radiometer calibration, 0

2

Iλ σ , as is the case with land-based

units.

The uncertainty in each individual measurement of N Iλ ,

and hence aλ τ and α , can be computed by specifying the

pitch, roll, heading, latitude, longitude, and time using the

model we have developed. This enables filtering schemes to

be devised on the basis of uncertainty. An example of the use

of this technique is shown in Figure 11.2 using data collected

on the R/V Ron Brown from the ACE-ASIA experiment. As

the figure shows, 0.03 στ < for 66% of the cloud-free

measurements taken during the experiment.

11.3 CONCLUSION

We believe that we have essentially perfected the marine

shadow-band technique and have learned how to track any

calibration drift that may occur. The size of the FRSR

database continues to grow rapidly and we have developed a

configuration control system to document all aspects of the

data. Each radiometer has a calibration document that is

always kept current and changes to calibrations are noted. Our

new technique to track calibration drift using the on-board

Eppley PSP is showing great promise. Furthermore, our

understanding of instrument uncertainties has become mature.

Thus, we are approaching a method-of-operation worthy of the

designation “marine shadow-band network”.

We are finally in a position to evaluate aλ τ used in

atmospheric correction models with the data we have collected

from the FRSR. We have an extensive database of aerosol

optical properties from over the world’s oceans and have

developed the method for quantifying the uncertainties

associated with the measurements. Two manuscripts

describing the FRSR database are in preparation and a third

describing satellite match-ups is planned. We anticipate that

all three manuscripts will be submitted within the next year.

The bottom line is that we believe we have a robust,

repeatable, and reliable technique for evaluating satellite data.

It is now time to begin thinking about additional

radiometric characteristics of the marine atmosphere. We

need to quantify the effects of widely scattered clouds on the

optical thickness observed by the satellite. Indeed, it may be

time to alter our thinking such that we begin speaking in terms

of optical thickness, rather than aerosol optical thickness. In

cirrus or widely scattered cumulus conditions, it may be

possible to accurately characterize the underlying ocean. If

we work toward improving our understanding of the cloud and
SIMBIOS Project Annual Report

82

aerosol system, as opposed to looking for completely clear

conditions, the capabilities of ocean color satellites may be

enhanced. Toward that end, we need to be looking past the

direct-normal irradiance measurements of the FRSR as the

principal tool for satellite evaluation. We need to begin to

analyze and understand the direct-normal irradiance as it is the

key to achieving a better understanding of the cloud and

aerosol system that is present in the marine atmosphere.

REFERENCES

Gordon, H.R. 1978: Removal of atmospheric effects from

satellite imagery of the oceans. Applied Optics, 17, 1631-

1636.

Gordon, H.R., D.K. Clark, J.L. Mueller, and W.A. Hovis

1980: Phytoplankton pigments derived from the Nimbus7 CZCS: initial comparisons with surface measurements.

Science, 210, 63-66.

Gordon, H.R., and D.K. Clark 1981: Clear water radiances for

atmospheric correction of coastal zone color scanner

imagery, Applied Optics, 20, 4175-4180.

Gordon, H.R. and M.Wang 1994: Retrieval of water-leaving

radiance and aerosol optical thickness over the oceans

with SeaWiFS: a preliminary algorithm. Applied Optics,

33, 443-452.

Reynolds, M.R., M.A. Miller, and M.J. Bartholomew, 1999: A

fast-rotating, spectral shadowband radiometer for marine

applications, J. Atmos. Ocean. Tech.,18, 200-214.

Thuiller, G., M. Herse, P.C. Simon, D. Labs, H. Mandel, and

D. Gillotay, 1998: Observations of the solar spectral

irradiance from 200 nm to 870 nm during the ATLAS 1

and ATLAS 2 missions by the SOLSPEC spectrometer,

Metrologia, 35, 689-695.
SIMBIOS Project Annual Report

83

Table 11.2: FRSR deployments

Cruise Ship Location Status Duration

Aero99 Ron Brown Atlantic Oceans SEABASS 1 month

Ant00 Polarbird Antarctic Radiation-no FRSR 1.5 months

Ex0127-138 Exp. Seas Caribbean SEABASS 2.5 months

Ex0139-140 Exp. Seas Caribbean Data just received 2 weeks

Indo99 Ron Brown Indian Ocean SEABASS 1 month

Ka0008 Ka’imimoan Tropical Western Pacific SEABASS 1 month

Ka0009 Ka’imimoan Tropical Western Pacific SEABASS 1 month

Ka0101 Ka’imimoan Tropical Western Pacific Not processed 5-days

Ka0102 Ka’imimoan Hawaii to Southern California SEABASS 1 month

Ka0104 Ka’imimoan Southern California to Hawaii SEABASS 10 days

Ka0105 Ka’imimoan Hawaii to Tropical Pacific SEABASS 1 month

Ka0106 Ka’imimoan Tropical Pacific SEABASS 1 month

Ka0107 Ka’imimoan Tropical Western Pacific SEABASS 1 month

Kn01 Knorr Tropical Atlantic SEABASS 2 months

Mel99 Melville Gulf of California SEABASS 20 days

Mr00k3 Mirai Sea of Japan to North Pacific SEABASS 2 months

Mr00k4 Mirai Japan to Tropical Western Pacific SEABASS 1 month

Mr00k5 Mirai Japan to Western Pacific SEABASS 1 month

Mr00k6 Mirai Tropical Western Pacific In processing 1 month

Mr00k7 Mirai Japan to Eastern Indian Ocean In processing-no FRSR 1 month

Mr00k7a Mirai Japan to Tropical Western Pacific In processing-no FRSR 1 month

Mr00k8 Mirai Tropical Western Pacific In processing 1 month

Mr01k1 Mirai Tropical Western Pacific to Japan In processing 1 month

Mr01k2 Mirai Sea of Japan In processing 1 months

Mr01k3 Mirai Sea of Japan to North Pacific In processing 1.5 months

Mr01k4 Mirai North Pacific to Sea of Japan Data just received 1 month

N99mr Mirai Tropical Western Pacific SEABASS 1 month

N99rb Ron Brown Tropical Western Pacific SEABASS 1 month

N99na Nauru Tropical Western Pacific SEABASS 1 month

Pstar00 Polar Star Australia to California SEABASS 2 months

Rb0009 Ron Brown Southern California to Tropics SEABASS 1 month

Rb0100 Ron Brown South Carolina to South Florida Radiation-no FRSR 1 month

Rb0101 Ron Brown Equatorial Pacific SEABASS-2 FRSRs 1 month

Rb0102 Ron Brown Mid-Pacific to Japan SEABASS-2 FRSRs 1 month
SIMBIOS Project Annual Report

84

Chapter 12

Bio-Optical Measurement and Modeling of the California Current

and Southern Oceans

B. Greg Mitchell

Scripps Institution of Oceanography, University of California San Diego, La Jolla, California

12.1 INTRODUCTION

This SIMBIOS project contract has supported in situ

ocean optical observations in the California Current, and the

north Pacific, Southern and Indian Oceans. Our principal

goals are to validate standard or experimental ocean color

products through detailed bio-optical and biogeochemical

measurements, and to combine ocean optical observations

with modeling to contribute to satellite vicarious radiometric

calibration and algorithm development.

In collaboration with major oceanographic ship-based

observation programs (CalCOFI, JGOFS, AMLR, INDOEX,

and ACE Asia) our SIMBIOS effort has resulted in data from

diverse bio-optical provinces. For these global deployments

we generate a methodologically consistent data set

encompassing a wide-range of oceanic conditions. We have

initiated several collaborations with scientists in East Asian

countries to study the complex Case 2 waters of their marginal

seas. Global data collected in recent years have been

integrated with our on-going CalCOFI time-series. The

combined database we have assembled now comprises more

than 800 stations and includes observations for the clearest

oligotrophic waters, highly eutrophic blooms, red-tides and

coastal case 2 conditions. The data has been used to validate

water-leaving radiance estimated with SeaWiFS as well as

bio-optical algorithms for chlorophyll pigments. The

comprehensive data is utilized for development of standard

and experimental algorithms.

12.2 RESEARCH ACTIVITIES

We continue to participate on CalCOFI cruises to the

California Current System (CCS) for which we have an 8-year

time-series. This region experiences a large dynamic range of

coastal and open ocean trophic structure and has experienced

strong interannual forcing associated with the El Niño – La

Niña cycle from 1997-2000 (Kahru and Mitchell, 2000; Kahru

and Mitchell, 2001a). CalCOFI data provides an excellent

reference for evaluating our other global data sets (O’Reilly et

al. 1998).

During the third year of our contract, we participated in 3

CalCOFI cruises, one cruise in collaboration with NOAA

AMLR to the Southern Ocean, and the ACE Asia cruise to the

northwest Pacific on NOAA R/V Ronald Brown. The global

distribution of our present data set is shown in Figure 12.1.

On most cruises, an integrated underwater profiling system

was used to collect optical data and to characterize the water

column. The system included an underwater radiometer

(Biospherical Instruments MER-2040 or MER-2048)

measuring depth, downwelling spectral irradiance (Ed) and

upwelling radiance (Lu) in 13 spectral bands. A MER-2041

deck-mounted reference radiometer (Biospherical

Instruments) provided simultaneous measurements of abovesurface downwelling irradiance. Details of the profiling

procedures, characterization and calibration of the

radiometers, data processing and quality control are described

in Mitchell and Kahru (1998). The underwater radiometer

was also interfaced with 25 cm transmissometers (SeaTech or

WetLabs), a fluorometer, and SeaBird conductivity and

temperature probes. When available, additional

instrumentation integrated onto the profiling package included

AC9 absorption and attenuation meters (WetLabs Inc.), and a

Hydroscat-6 backscattering meter (HobiLabs). For the AMLR

and ACE Asia cruises in 2001 we deployed our new

Biospherical Instruments PRR 800 free fall radiometer that

included 19 channels of surface irradiance 312- 865 nm and

three geometries of underwater radiometry (Lu, Eu, Ed) from

312-700 nm. We have shown that measuring these three

radiometric geometries with our MER 2048 allowed us to

retrieve backscatter and absorption coefficients (Stramska et

al., 2000).

At in situ optical stations discrete water samples were

collected from a CTD-Rosette immediately before or after

each profile for additional optical and biogeochemical

analyses. Pigment concentrations were determined

fluorometrically and with HPLC. All HPLC samples acquired
SIMBIOS Project Annual Report

85

Figure 12.1: Global distribution of bio-optical stations

accomplished in the past 5 years by the Scripps Photobiology

Group (SPG). All stations include spectral reflectance and

fluorometric chlorophyll. Most include particle and soluble

absorption and HPLC pigments. For some CalCOFI and all

international cruises since 1997 we have deployed Hydroscat

backscatter and AC9 absorption and attenuation meters to

better understand the variables that govern remote sensing

reflectance.

in the past year have been submitted to San Diego State

University for analysis under a separate SIMBIOS contract.

Spectral absorption coefficients (300-800 nm) of particulate

material were estimated by scanning particles concentrated

onto Whatman GF/F filters (Mitchell 1990) in a dual-beam

spectrophotometer (Varian Cary 1). Absorption of soluble

material was measured in 10 cm cuvettes after filtering

seawater samples through 0.2 μm pore size polycarbonate

filters. Absorption methods are described in more detail in

Mitchell et al. (2001a). For some cruises we collected

measurements of other optical and phytoplankton properties

including photosynthesis, particulate organic matter (carbon

and nitrogen), phycoerythrin pigment, and size distribution

using flow cytometry and a Coulter Multisizer.

12.3 RESEARCH RESULTS

Our normalized water leaving radiances (Lwn) at

SeaWiFS bands for global data, except Southern Ocean, are

plotted against surface chl-a in Figure 12.2A. Data collected

during these cruises represents approximately 25% of the data

used for development of the SeaWiFS OC4v4 algorithm

(O’Reilly et al., 2000). Kahru and Mitchell (1999; 2001a)

have shown that the chl-a retrieved by satellite in the CalCOFI

region is in good agreement with our ship data when

computed with our regional algorithm or NASA’s OC4v4.

Southern Ocean results for in situ observations during JGOFS

(1997-1998) and AMLR (2000-2001) indicate a strong

deviation from NASA’s OC4v4 algorithm relationship for chla in the range 0.l-2.0 mg m-3 (Figure 12.2B). This region has

been shown to have bio-optical algorithms that are different

than low latitude regions such as CalCOFI (Mitchell and

Holm-Hansen, 1991; Mitchell, 1992; Arrigo et al., 1998;

Dierssen and Smith, 2000). We have developed a new

Southern Ocean chl-a algorithm based on an OC4-type fit to

the Southern Ocean data set (Figure 12.2B; Mitchell et al.,

2001b). These results underscore the need for more data to

serve as a basis for regional algorithms to improve estimates

of chl-a from ocean color remote sensing. A global ocean

color data processing scheme needs to be specified that can

transition from low latitude to high latitude without arbitrary

discontinuities. More data and advanced models are required

to resolve issues regarding Southern Ocean bio-optical

algorithms and to understand the causes of observed

differentiation. For a better understanding, it is essential to

determine not only reflectance and chlorophyll, but also

inherent optical properties including absorption and

backscattering as reported by Reynolds et al. (2001).

Generally, there are few observations in the Southern Ocean,

and even fewer with detailed observations including inherent

optical properties. During our AMLR cruise in January 2002

we will deploy our Hydroscat backscatter meter and collect

water samples for measurement of particle and soluble

absorption, HPLC pigments and particle size distribution to

extend our understanding of Southern Ocean algorithm issues.

Processing SeaWiFS data with SeaDAS v4.1 still results

in significant underestimates of Lwn at 412 and 443 for some

of our case 1 water match ups with high resolution HRPT

data. Figure 12.3 shows spectral Lwn for two ACE Asia

stations, both in Case 1 water types with moderate chl-a

concentrations (0.3 – 0.5 mg chl-a m-3). The offshore station

between Hawaii and Japan had reasonable retrieval of spectral

Lwn (Figure 12.3A) but the Kuroshio intrusion station

between Japan and Korea did not (Figure 12.3B). We believe

this difference is caused by errors in the aerosol models in the

atmospheric correction for SeaWiFS for atmospheres that have

significant terrestrial or anthropogenic aerosols. Continued

research on atmospheric aerosols is required to improve the

accuracy of Lwn if we are to be able to apply multiwavelength bio-optical retrieval algorithms that require

accurate estimates of Lwn at 412 and 443 (e.g. Garver and

Siegel, 1997; Carder et al., 1999). Collaborations with

aerosols chemists and atmospheric optics experts will be

expanded in the next year using ACE Asia and INDOEX data

sets.

With colleagues in Korea, China, Japan and Hong Kong

we have assembled a large regional data set from the East

China Sea, South China Sea and coastal waters of Eastern
SIMBIOS Project Annual Report

86

Asia. Figure 12.4 shows reflectance ratios for SeaWiFS bands

plotted against chl-a for CalCOFI and Asian data sets. Where

the Asian data deviate, it is usually toward a lower ratio

compared to CalCOFI. One dramatic difference between the

East Asian data and our CalCOFI data set is the very high

variance in the relationship between particle absorption and

chl-a (Figure 12.5). Soluble absorption in the region also

tends to be higher than for CalCOFI (data not shown). The

reflectance trends for Asian data (Figure 12.4) are consistent

with higher absorption that is likely due both to detrital and

soluble absorption. This recently merged data set is being used

to evaluate Case 2 algorithms in collaboration with our Asian

colleagues (Kahru et al., 2001b).

 12.4 FUTURE WORK

We will continue our approach of acquiring detailed data

sets at the global scale for ocean color satellite validation and

algorithm development. CalCOFI will continue as our core

field program and we plan to complete one detailed CalCOFI

cruise per year and 3 that have a minimal set of chl-a and

reflectance data. We are advising the CalCOFI program on

concepts for a permanent optics program to be integrated into

their routine CTD profiling. A close cooperation has been

initiated with the IMEMECOCAL program off Baja California

that is coordinated by scientists from CICESE in Ensenada,

Mexico. Data from their cruises starting in 1997 will be

available to us in 2002. The Southern Ocean and East Asian

marginal seas will continue to be a high priority since it is

evident that standard algorithms for chl-a do not perform well

in these regions. In January, 2002 we will participate in the

NOAA AMLR cruise to the Southern Ocean. During 2002

we will organize a SIMBIOS workshop to address polar

algorithm issues. Modeling efforts will continue to improve

our understanding of regional bio-optical properties and their

relationship to biogeochemical parameters (e.g. Stramska et

al., 2000; Reynolds et al, 2001; Loisel et al., 2001). The

models and data will contribute to development of advanced

algorithms and parameterizations for semi-analytical inversion

models for the retrieval of inherent optical properties and

biogeochemical properties besides chl-a.

REFERENCES

Arrigo, K.R., D.H. Robinson, D.L. Worthen, B. Schieber and

M.P. Lizotte 1998: Bio-optical properties of the

southwestern Ross Sea. Journal of Geophysical Research.

103(C10): 21,683-21,695.

Carder, K.L., F.R. Chen, Z.P. Lee and S.K. Hawes 1999:

Semianalytic moderate-resolution imaging spectrometer

algorithms for chlorophyll a and absorption with biooptical domains based on nitrate-depletion temperatures.

Journal of Geophysical Research. 104(C3): 5,403-5,421.

Dierssen, H.M. and Smith, R.C. 2000: Bio-optical properties

and remote sensing ocean color algorithms for Antarctic

Peninsula waters, Journal of Geophysical Research,

105(C11): 26301-26312.

Garver, S.A. and D.A. Siegel 1997: Inherent Optical Property

Inversion of Ocean Color Spectra and its Biogeochemical

Interpretation: 1. Time series from the Sargasso Sea.

Journal of Geophysical Research, 102(C8): 18,607-

18,625.

Kahru, M. and B.G. Mitchell 1999: Empirical chlorophyll

algorithm and preliminary SeaWiFS validation for the

California Current. International Journal of Remote

Sensing. 20(17): 3,423-3,430.

Kahru, M. and B.G. Mitchell 2000: Influence of the 1997-98

El Niño on the surface chlorophyll in the California

Current. Geophysical Research Letters. 27(18): 2,937-

2,940.

Kahru, M. and B.G. Mitchell 2001a: Seasonal and nonseasonal variability of satellite-derived chlorophyll and

colored dissolved organic matter concentration in the

California Current. Journal of Geophysical Research..

106(C2): 2517-2529.

 Kahru, M., B. G. Mitchell, J. Wieland, J. C. Chen, N. W.

Man, and Y. S. Suh 2001b: Validation of Ocean Color

Algorithms in East Asian Marginal Seas. American

Geophysical Union Conference. San Francisco. 10-14

December, 2001.

Loisel, H. and D. Stramski 2000: Estimation of the inherent

optical properties of natural waters from the irradiance

attenuation coefficient and reflectance in the presence of

Raman scattering. Applied Optics. 39(18): 3,001-3,011.

Mitchell, B.G. 1990: Algorithms for determining the

absorption coefficient of aquatic particulates using the

quantitative filter technique (QFT). In: Spinrad, R., ed.,

Ocean Optics X, Bellingham, Washington, SPIE, 137-

148.
SIMBIOS Project Annual Report

87

Mitchell, B.G. 1992: Predictive bio-optical relationships for

polar oceans and marginal ice zones. Journal of Marine

Systems. 3: 91-105.

Mitchell, B.G. and M. Kahru 1998: Algorithms for SeaWiFS

standard products developed with the CalCOFI biooptical data set. Cal.Coop.Ocean.Fish.Invest. R. 39: 133-

147.

Mitchell, B.G. and O. Holm-Hansen, 1991: Bio-optical

properties of Antarctic Peninsula waters: Differentiation

from temperate ocean models. Deep-Sea Research I.

38(8/9): 1,009-1,028.

Mitchell B. G. , M. Kahru, J. Wieland and M. Stramska

2001a: Determination of spectral absorption coefficients

of particles, dissolved material and phytoplankton for

discrete water samples. In: NASA, Ocean Optics

Protocols for Satellite Ocean Color Sensor Validation.

Mitchell, B. G., M. Kahru, R. Reynolds, J. Wieland, D.

Stramski, C. Hewes, O. Holm-Hansen 2001b: Evaluation

of Chlorophyll-a Ocean Color Algorithms for the

Southern Ocean. Amerian Geophysical Union. San

Francisco. 10-14 December, 2001.

O'Reilly, J.E., S. Maritorena, B.G. Mitchell, D.A. Siegel, K.L.

Carder, S.A. Garver, M. Kahru and C. McClain 1998:

Ocean color chlorophyll algorithms for SeaWiFS. Journal

of Geophysical Research. 103(C11): 24,937-24,953.

O'Reilly, J.E., S. Maritorena, D.A. Siegel, M.C. O'Brien, D.A.

Toole, B.G. Mitchell, M. Kahru, et al. 2000: Ocean color

chlorophyll a algorithms for SeaWiFS, OC2 and OC4:

Version 4. In: NASA, SeaWiFS Postlaunch Calibration

and Validation Analyses.

Reynolds, R.A., D. Stramski and B.G. Mitchell 2001: A

chlorophyll-dependent semianalytical reflectance model

from derived from field measurements of absorption and

back scattering coefficients within the Southern Ocean.

Journal of Geophysical Research. 106(C4): 7125-7138.

Siegel, D.A., M. Wang, S. Maritorena and W. Robinson 2000:

Atmospheric correction of satellite ocean color imagery:

The black pixel assumption. Appl.Opt. 39(21): 3,582-

3,591.

Stramska, M., D. Stramski, B.G. Mitchell and C.D. Mobley

2000: Estimation of the absorption and backscattering

coefficients from in-water radiometric measurements.

Limnology and Oceanography. 45(3): 628-641.
SIMBIOS Project Annual Report

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Figure 12.2: A. Maximum band ratio algorithms for the SPG Case 1 global data set excluding our Southern

Ocean JGOFS and AMLR data.. These data comprise approximately 25% of NASA’s SeaWiFS chl-a algorithm

data set used by O’Reilly et al. (2000). The lines are fits for NASA’s SeaWiFS OC4v4 and our OC4-type fit to

the SPG data set. Minor differences between OC4v4 and OC4-SPG fit to our data are evident for chl-a between

0.3 – 3.0 mg m-3.are evident. B. Spectral reflectance band ratios plotted against chl-a for our Southern Ocean

data set. The curved line through the data is our SPG-ANT relationship for the Southern Ocean and the curved

line that does not fit the data is NASA’s standard OC4v4 relationship. The linear line is the Mitchell (1992)

B

A
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Figure 12.3: Scatter plots of ocean spectral reflectance ratios for various SeaWiFS bands plotted against chl-a for

our CalCOFI (◊) and East Asian region (X) data sets. CalCOFI data have a coherent relationship, but some of the

East Asian waters diverge due to complexity of Case 2 water constituents including detrital absorption, CDOM and

mineral particles.
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Figure 12.4: Spectral absorption by ocean particles plotted against chl-a for CalCOFI (•) and our East

Asian data set (x). A. 440 nm; B. 676 nm. The line is a power fit to our CalCOFI data. Some of the

Asian data is consistent with CalCOFI, but many near-coastal or shelf stations exhibit much higher

absorption per unit chl-a than CalCOFI due to detrital pigments. This strong blue absorption will make

retrieval of chl-a from passive reflectance in the blue very difficult in these waters since the dominant

absorption will be from detritus and soluble material rather than chl-a and associated photosynthetic

pigments.

A

B
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A

B

Figure 12.5: Lwn matchups between ACE Asia in situ radiometry (x) and SeaWiFS (◊) in the open north

Pacific (A) and in the Kuroshio intrusion between Japan and Korea (B). Both had similar chl-a

concentrations and were Case 1 waters. The large discrepancy in the east Asian region is attributed to

errors in the aerosol models for SeaWiFS atmospheric corrections.
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Chapter 13

Variability in Ocean Color Associated with Phytoplankton and

Terrigenous Matter: Time Series Measurements and Algorithm

Development at the FRONT Site on the New England

Continental Shelf.

John R. Morrison and Heidi M. Sosik

Woods Hole Oceanographic Institution, Woods Hole, Massachusetts

13.1 INTRODUCTION

Fronts in the coastal ocean describe areas of strong

horizontal gradients in both physical and biological properties

associated with tidal mixing and freshwater estuarine output

(e.g. Simpson, 1981 and O’Donnell, 1993). Related gradients

in optically important constituents mean that fronts can be

observed from space as changes in ocean color as well as sea

surface temperature (e.g., Dupouy et al., 1986). This research

program is designed to determine which processes and

optically important constituents must be considered to explain

ocean color variations associated with coastal fronts on the

New England continental shelf, in particular the National

Ocean Partnership Program (NOPP) Front Resolving

Observational Network With Telemetry (FRONT) site. This

site is located at the mouth of Long Island sound and was

selected after the analysis of 12 years of AVHRR data showed

the region to be an area of strong frontal activity (Ullman and

Cornillon, 1999). FRONT consists of a network of modem

nodes that link bottom mounted Acoustic Doppler Current

Profilers (ADCPs) and profiling arrays. At the center of the

network is the Autonomous Vertically Profiling Plankton

Observatory (AVPPO) (Gallager et al. 1998, Thwaites et al.

1998). The AVPPO consists of buoyant sampling vehicle and

a trawl-resistant bottom-mounted enclosure, which holds a

winch, the vehicle (when not sampling), batteries, and

controller. Three sampling systems are present on the vehicle,

a video plankton recorder, a CTD with accessory sensors, and

a suite of bio-optical sensors including Satlantic OCI-200 and

OCR-200 spectral radiometers and a WetLabs ac-9 dual path

absorption and attenuation meter. At preprogrammed times

the vehicle is released, floats to the surface, and is then

winched back into the enclosure with power and data

connection maintained through the winch cable.

Communication to shore is possible through a bottom cable

and nearby surface telemetry buoy, equipped with a mobile

modem, giving the capability for near-real time data

transmission and interactive sampling control.

13.2 RESEARCH ACTIVITIES

AVPPO deployments

During the first year of the contract the bio-optical

sensors were successfully integrated with the rest of the

systems on the AVPPO with extensive tests both on shore and

in the test well on the Woods Hole Oceanographic Institution

(WHOI) dock. Two deployments were made prior to October

1, 2001, with another scheduled for October 4, 2001.

The AVPPO was initially deployed during cruise

CT001206 on December 8, 2000, from the R/V Connecticut at

the FRONT site (40.9922 °N, -79.7332 ° W) in 32 m of water.

The unit functioned successfully sending back both

environmental and bio-optical data after each profile. The

telemetry buoy was equipped with a surface reference

radiometer (Satlantic, OCI-200 sensor with Stor-Dat data

logger) and single-wavelength combined absorption and

optical backscattering sensor (HOBILabs, a-eta). An ADCP

was mounted on the AVPPO bottom enclosure. During the

deployment cruise a number of radiometer casts using a

Satlantic SeaWiFS Profiling Multichannel Radiometer

(SPMR) were made with accompanying CTD and rosette

water sample profiles. Discrete water samples were collected

for determination of the concentrations of pigments (both

HPLC and fluoremetric assays), suspended particulate matter

(SPM), and nutrients, as well as particulate and colored

dissolved organic matter (CDOM) absorption. At midday on

Tuesday December 12, communications with the mooring

were lost during a scheduled activity period. Meteorological

conditions at the time were severe with winds in excess of 60

miles per hour. Communications were regained at the next

scheduled activity, six hours later, but showed that the
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controller, on the bottom-mounted enclosure, was unable to

talk to all instruments on the buoyant vehicle. This continued

to be the case with all subsequent activity periods. On

December 19, the next available weather window and after

another bad storm, the mooring was recovered during cruise

CT001219. The buoyant profiler was absent with the cable

severed. All other components of the system were intact. The

profiler was discovered partially buried on a beach on Block

Island on January 1, 2001. Some damage was apparent upon

inspection of the instruments after their return to WHOI,

including some scratches on the radiometer heads. All

instruments were subsequently returned to the manufacturers

for repair and calibration. During this deployment 47 profiles

were made.

The second deployment was during cruise CT010912 on

September 13, 2001 from the R/V Connecticut at a site in

Massachusetts Bay (42.2423 °N, -70.5571 °W) in 44 m of

water. Discrete water samples were collected from a CTD

rosette for pigment and IOP measurements. The AVPPO was

recovered at the end of the cruise on September 20, 2001.

During this deployment 81 profiles were made.

Additional Cruises

Bio-optical data was also collected from an additional

cruise, EN355 (May 31, 2001 to June 13, 2001). 11 optical

stations were performed which included included; SPMR

casts, CTD casts with discrete water samples for pigment and

inherent optical property (IOP) measurements, absorption and

attenuation profiles with ac-9’s, and backscatter and

absorption measurements with an a-eta.

13.3 RESEARCH RESULTS

From the initial four day AVPPO deployment, at the

FRONT site, hydrographic data from the mooring showed the

regular occurrence of colder, less saline water at semi-diurnal

timescales, Figure 13.1A-B. This was also apparent in the

bio-optical data, from both the fluorometer, Figure 13.1C, and

the IOPs from the ac-9 on the AVPPO, Figure 13.1D-F, which

showed that the colder less saline water had the greatest

attenuation and absorption. Decomposition of the spectral

absorption measured by the ac-9, achieved by assigning

characteristic shapes to algal and non-algal fractions and

minimizing the sum of square deviations between predicted

and observed spectra, suggested that the majority of the

variability in the optical signal was due to changes in the nonalgal component, Figure 13.1G-H. The non-algal slope, S,

was also retrieved using the decomposition and was within the

range of previously observations (mean=0.0120 nm-1,

std=2.5×10-4, N=1274). The ADCP mounted on the AVPPO

bottom enclosure indicated that the colder, less saline water

was associated with a southeasterly flow, Figure 2. Both a

semi-diurnal and diurnal component were seen in the current

measurements indicated by the greater magnitudes of the

southerly flows around the middle of the days. Fourier

transformation of the sea surface elevation showed the

presence of M2, S2, O1, and K1 tidal harmonics.

The remote sensing reflectance from the AVPPO, an

Apparent Optical Property (AOP) obtained from

measurements using OC-200 radiometer heads, showed two

distinct shapes most clearly seen by the ratio of 550 to 510

nm, Figure 13.3A. On all days, higher ratios were associated

with colder, less saline waters and lower ratios with warmer

more saline waters, Figure 13.3B. Most of the variability in

the ratio appeared to be associated with variations in the nonalgal component from decomposition of spectral absorption

measurements, Figure 13.3C. There appeared to be little

relationship with algal absorption estimates from the

decomposition, Figure 13.3D.

Initial results from the Massachusetts Bay deployment

again showed changes in bio-optical properties on tidal

timescales, Figure 13.4. Semi-diurnal events appeared to

rapidly move surface waters to depths of 35 m.

13.4 CONCLUSIONS

Results from the first two deployments of the AVPPO

have shown the potential for a profiling mooring to study the

variability associated with the complex dynamics of coastal

environments including fronts. During the observation period

at the FRONT site the change in hydrographic and bio-optical

properties can be explained by the influx of hydrographically

and optically different water from Long Island Sound on tidal

timescales. The rapid change in properties is indicative of a

coastal ocean front. The contiguous measurement of both

IOPs and AOPs in differing water masses over short periods

of time will be useful in future algorithm development and

validation. The deployments also highlighted some of the

difficulties of operating complex oceanographic equipment in

coastal environments.

REFERENCES

Dupouy, C., J.P. Rebert, and D. Toure, 1986: Nimbus-7

coastal zone color scanner pictures of phytoplankton growth

on an upwelling front in Senegal. In: Marine Interfaces

Ecohydrodynamics, J.C.J. Nihoul (Ed.), Elsevier Science

Pub., 617-664.

Gallager, S. M., F. T. Thwaites, C. S. Davis, A. M. Bradley

and A. Girard. 1998: Time series measurements in the

coastal ocean: The Autonomous Vertically Profiling

Plankton Observatory (AVPPO), Sea Technology.
SIMBIOS Project Annual Report

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O’Donnell, J., 1993: Surface Fonts in Estuaries: A Review.

Estuaries. 16, 12-39.

Simpson, J. H., 1981: The shelf-sea fronts: implications of

their existence and behaviour. Phil. Trans. R. Soc. Lon. A,

302, 531-546.

Thwaites F. T., S. M. Gallager, C. S. Davis, A. M. Bradley, A.

Girard and W. Paul. 1998: A winch and cable for the

autonomous vertically profiling plankton observatory, Proc.

Oceans, 98. 

Ullman, D.S., and P.C. Cornillon, 1999: Satellite-derived sea

surface temperature fronts on the continental shelf off the

northeast U.S. coast. J. Goephys. Res., 104, 23459-

23478.

This research was supported by

SIMBIOS NASA contract #00198

PRESENTATIONS

Morrison, J.R., Sosik, H.M., Codiga, D.L., Gallager, S.M., and

Fargion, G.S., 2001: Time Series Measurements and

Algorithm Development at the FRONT Site on the New

England Continental Shelf, EOS. Trans. AGU, 82(47),

Fall Meet. Suppl., Abstract # OS52A-0525 , 2001
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Figure 13.1: Interpolated hydrographic and bio-optical data from the first deployment of the AVPPO at the FRONT site. A and

B show the semi-diurnal influx of colder, less saline waters at the surface, and C, the fluorometer output. The IOPs from the

ac-9, D to F, also show semi-diurnal variation. Decomposition of the spectral absorption measured by the ac-9 suggested that

the majority of the variability in the optical signal was due to changes in the non-algal component, G and H (absorption values

are at 440 nm). The non-algal slope was also retrieved, I. Black dots indicate measurement positions used for the

interpolations.
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Figure 13.2: Current vectors (white arrows), measured using the ADCP mounted on the AVPPO bottom enclosure,

superimposed upon interpolated temperature data indicated that the colder less saline water was associated with a southeasterly

flow.
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Figure 13.3: The remote sensing reflectance from the AVPPO showed two distinct shapes associated with ‘cold’, x with solid

line, and ‘warm’ waters, o with dot-dashed line, A. On all days, higher ratios of the ratio of remote sensing reflectances at 550

to 510 nm were associated with colder less saline waters and lower ratios with warmer more saline waters, B. The relationships

of the 550 to 510 ratio with the non-algal, adm, C, and phytoplankton, aph, absorptions.
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Figure 13.4: Interpolated Inherent Optical Properties from the AVPPO ac-9 from the Massachusetts Bay deployment show a

semi-diurnal movement to depth of surface waters, A to C. Black dots indicate measurements positions used for the

interpolations.
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Chapter 14

Ocean Optics Protocols and SIMBIOS Protocol Intercomparison

Round Robin Experiments (SPIRREX).

James L. Mueller

Center for Hydro-Optics and Remote Sensing, San Diego State University, California

14.1 INTRODUCTION

The objective of research under this contract is the

maintenance and annual revision of the document “Ocean

Optics Protocols for Satellite Ocean Color Validation (e.g.

Fargion and Mueller 2000).

14.2 RESEARCH ACTIVITIES

The research activities under the first year of this contract

were devoted to the preparation of “Ocean Optics Protocols

for Satellite Ocean Color Validation, Revision 3” (Mueller et

al. 2002), building on Revision 2 (Fargion and Mueller 2000).

The important updates, additions and revisions in the Revision

3 document are summarized in the next section.

14.3 RESEARCH RESULTS

Revision 3 to the ocean optics protocol document

(Mueller et al. 2002) comprises several significant changes

compared to Revision 2 (Fargion and Mueller 2000). The

most obvious of these are 3 new chapters, and the resulting

renumbering of the other chapters to reflect the new insertions

in the document. The new chapters are:

Chapter 2, “Fundamental Definitions, Relationships and

Conventions”, introduces the radiometric quantities, inherent

optical properties, fundamental concepts and terminology

underlying the in situ measurement and analysis protocols

discussed throughout the document. The chapter also

discusses the scales adopted in these protocols for such

quantities as extraterrestrial solar irradiance, and the

absorption and scattering coefficients of pure water.

Chapter 11, “MOBY, A Radiometric Buoy for

Performance Monitoring and Vicarious Calibration of Satellite

Ocean Color Sensors: Measurement and Data Analysis

Protocols”, by Clark et al, was added to document the specific

measurement and data analysis protocols used in the operation

of this critical radiometric observatory. The MOBY

normalized water-leaving radiance time series has provided

the principal, common basis for vicarious calibration of

satellite ocean color sensors. The material is Chapter 11 is

provided background information essential for investigators

combining MOBY and satellite sensor data for validation

studies.

Chapter 13, “Normalized Water-Leaving Radiance and

Remote Sensing Reflectance: Bidirectional Reflectance and

Other Factors”, by Morel and Mueller, explains the physics

underlying the bidirectionality of the ocean’s reflectance and

water-leaving radiance. An outline is then presented

describing the general methods for accounting for and

removing bidirectional effects from exact normalized waterleaving radiance, together with a simplified method of its

determination for Case 1 waters using lookup tables that are

available on-line over the INTERNET. The information in

this chapter is critical, because the only form of water-leaving

radiance that allows a valid match-up comparison between

satellite and in situ determinations is exact normalized waterleaving radiance.

Several of the renumbered chapters carried over from

Revision 2 were significantly revised, while others were

modified only slightly. The overviews of instrument

characteristics and associated characterization methods

(Chapter 4) and field measurements and data analysis (Chapter

9) were revised to reflect the changed content of the more

detailed chapters in those two parts of the document. Chapter

15, was revised to provide more focused protocols for

spectrophotometric determinations of absorption by particles

and dissolved materials in seawater samples, citing the

workshop results presented in the Revision 2 version where

needed. The chapters describing methods for HPLC

measurements of phytoplankton pigments (Chapter 16) and

fluorometric chlorophyll a (Chapter 17) were updated and

revised. The procedures for calibrating and characterizing

radiometers (Chapter 6) were modified to clarify the

procedural details. Methods for calibrating and using, sun

photometers and sky radiance instruments (Chapters 7 and 14)

were also revised, but will require further updates in Revision

4. The remaining chapters were also edited for consistency,

but the substance of these chapters remains intact.
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14.4 FUTURE WORK

Some of the deficiencies and corrective steps that will be

taken in Revision 4, scheduled for completion in 2002,

include: Protocols for instrument calibration and methods of

measurement and analysis for inherent optical properties

(IOP), which are currently summarized briefly in Chapters 4

and 9,will be addressed in a new chapter in Revision 4.

Another new chapter will be added to Revision 4

describing protocols for radiometric and bio-optical

measurements from moored and drifting buoys. These

methods will differ in many respects from the specialized

MOBY vicarious calibration observatory (Chapter 11 of

Revision 3), because of the different objectives and

deployment circumstances associated with these types of

buoys.

Protocols for radiometric measurements from aircraft,

although mentioned in the Revision 3 protocols, are not

provided in any detail. A new chapter on this topic will be

included in Revision 4.

Existing chapters will also be revised. Chapter 6, for

example, will be revisited and updated to develop a strategy

for moving the SIMBIOS community to calibrations based on

the NIST 2000 detector based scale of spectral irradiance, and

the consequences and importance of NIST Spectral Irradiance

and Radiance responsivity Calibrations with Uniform Sources

(SIRCUS) facility. Other examples of planned revisions

include the expansion of Chapter 2 to include atmospheric

radiometric quantities and processes relevant to ocean color,

and additions to Chapter 10 to address extrapolation of inwater profiles of upwelled radiance to the sea surface.

ACKNOWLEDGEMENTS

This research was supported under SIMBIOS NASA

Contract Number 00199.

REFERENCES

Fargion, G.S. and J.L. Mueller, 2000: Ocean Optics Protocols

for Satellite Ocean Color Sensor Validation, Revision 2,

NASA TM 2001-209955, NASA Goddard Space Flight

Center, Greenbelt, Maryland, 184 pp.

Mueller, J.L., et al., 2002: Ocean Optics Protocols for Satellite

Ocean Color Sensor Validation, Revision 3, NASA TM

2002-TBD, Mueller, J.L. and G. Fargion (Eds.), NASA

Goddard Space Flight Center, Greenbelt, Maryland, (In

Press).
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Chapter 15

Bermuda Bio Optics Project

Norm Nelson

Institute for Computational Earth Science, UCSB, Santa Barbara, California

15.1 INTRODUCTION

The Bermuda BioOptics Project (BBOP) is a

collaborative effort between the Institute for Computational

Earth System Science (ICESS) at the University of California

at Santa Barbara (UCSB) and the Bermuda Biological Station

for Research (BBSR). This research program is designed to

characterize light availability and utilization in the Sargasso

Sea, and to provide an optical link by which biogeochemical

observations may be used to evaluate bio-optical models for

pigment concentration, primary production, and sinking

particle fluxes from satellite-based ocean color sensors. The

BBOP time-series was initiated in 1992, and is carried out in

conjunction with the U.S. JGOFS Bermuda Atlantic Timeseries Study (BATS) at the Bermuda Biological Station for

Research. The BATS program itself has been observing

biogeochemical processes (primary productivity, particle flux

and elemental cycles) in the mesotrophic waters of the

Sargasso Sea since 1988. Closely affiliated with BBOP and

BATS is a separate NASA-funded study of the spatial

variability of biogeochemical processes in the Sargasso Sea

using high-resolution AVHRR and SeaWiFS data collected at

Bermuda (N. Nelson, P.I.). The collaboration between BATS

and BBOP measurements has resulted in a unique data set that

addresses not only the SIMBIOS goals but also the broader

issues of important factors controlling the carbon cycle.

15.2 RESEARCH ACTIVITIES

BBOP personnel participate on all BATS cruises, which

are conducted monthly with additional cruises during the

spring bloom period, January through May. Table 15.1

contains a list of data products relevant to SIMBIOS. The

BBOP project collects continuous profiles of apparent optical

properties (AOPs) in the upper 200m and deployments are

planned to optimize match-ups with the BATS primary

production incubations and with SeaWiFS overpasses. In

1999, a free-falling Satlantic profiling radiometer system

(SPMR/SMSR s/n 028) became our primary profiling

instrument. The primary optical measurements are

downwelling vector irradiance and upwelling radiance,

Ed(z,t,λ) and Lu(z,t,λ), respectively. Derived products

include remote sensing reflectance (Rrs(z,λ) and down- and

upwelled attenuation coefficients (Kd(z,λ), Ku(z,λ)). The

sampling package also includes a second mast-mounted

radiometer with wavebands matching those on the underwater

instrument for measuring incident downwelling vector

irradiance, Ed(0+,t,λ). This system was upgraded to 11

channels in 2000, and both instruments are calibrated three

times annually at UCSB. The profiler also includes a WetStar

fluorometer and Ocean Sensors CTD sensors. Its cable was

recently replaced after three years of service.

Bottle samples for fluorometric chlorophyll-a and

inherent optical properties (IOPs) are also collected.

Chlorophyll-a is collected once or twice daily during each

cruise. Discrete samples for determining the absorption

spectra of particulates, aph(z,λ) and ad(z,λ), and CDOM

(ag(z,λ)) are collected according to Nelson et al (1998).

Particulate absorption spectra are determined using the

quantitative filter technique (Mitchell, 1990) and CDOM

absorption according to Nelson et al (1998). Hyperspectral

observations of above water R+

rs(λ) are collected using the

Analytical Spectral Devices FieldSpec spectrometer (ASD,

Boulder CO). This instrument has begun experiencing

problems during the past year, most likely due to exposure to

corrosive salt spray at sea.

15.3 RESEARCH RESULTS

Careful attention to instrument behavior and calibration is

crucial to assuring high quality time-series data. We have

continued our efforts to determine the long-term calibration

behavior of our SPMR systems (sn028, owned by BBSR and

sn039, owned by BBOP) using several lamps traceable to our
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own NIST standard. Our past experience with radiometric

calibrations has shown that calibration coefficients used in the

field should be averages of many calibrations using several

different lamps (O’Brien et al, in press). We now have more

than 2 years of calibration data for SPMR-028 (one year in its

current configuration), and can begin to assess the long-term

variability of some of its channels. Figure 15.1 shows the

normalized calibration coefficients for several channels on one

irradiance head (SPMR system sn028, irradiance head 058),

including calibrations at both UCSB and Satlantic.

Calibration coefficients for most channels are within 3%, and

many are within 1%, even when comparing calibrations at

different laboratories. Not shown are two of the UV channels

(325 and 340nm), which often differ from the norm by as

much as 25%. Both irradiance and radiance heads have had

these two sensors replaced at least once since 1999. Since

lamp output is quite low in the UV, calibration results in this

range are extremely difficult to replicate, and it is not always

clear whether differences between individual calibrations are

due to sensor degradation or simply to lamp variability. The

growing lack of sensitivity in the UV channels may also be jue

to degradation of the cosine collector material (S. McLean,

pers. comm.). In addition, there has been some variability in

the behavior of our NIST standard, which must be resolved

before long-term averages can be computed (D. Menzies, pers.

comm.). Hence, all of our SPMR data submitted to date have

been processed using a single recent calibration rather than an

average, and will be updated when we can more accurately

describe the behavior of both the sensors and lamps, especially

in the UV. In March of 2001, the effects of immersion on

both our SPMR irradiance sensors were measured at CHORS.

We found significant differences between the measured and

factory-supplied immersion coefficients, and between the

immersion responses of the two instruments. As expected, the

values provided by the factory agreed well with CHORS

measurements in the red and green parts of the spectrum,

(within ~5%). In the blue and UV portions, the two differed

by as much as 20%. At wavelengths less than 380nm, the two

instruments showed large differences between their responses,

and only fair reproducibility (data not shown). After

application of the new immersion coefficients, Rrs0-

 values

measured with SPMR-028 agreed fairly will with those from

with MER-8733, for which immersion coefficients were

measured in 1998 (Figure 15.2). The peak in the reflectance

spectrum at 380 (Fig. 15.2b) and the unexpected difference

between the SPMR 028 and 039 spectra indicate that our

immersion coefficients may still be in error in the UV, and

that these measurements may need to be repeated.

We are continuing to examine the relationships between

retrieved chlorophyll a from SeaWiFS imagery and in situ

factors controlling optical properties, particularly chlorophyll

a and CDOM. One interesting result of this time series is that

SeaWiFS consistently overestimated in situ chlorophyll a

during the summer months of 1998 and 2000, but agreed well

in 1999 (Fig. 15.3a). An in situ explanation for this

phenomenon could be that unusually high CDOM

concentrations were present at the surface in 1998 and 2000,

leading to lower blue reflectance values. However, radiometric

data (Fig.15.3b-d) indicates that the algorithm is actually

responding to overly high green reflectances, which suggests

that the atmospheric correction is at least partially responsible.

The mid-latitude Sargasso Sea is subject to large inputs of

Saharan dust during the summer, and our current working

hypothesis is that inter-annual variability non-standard aerosol

spectra due to dust are responsible for the varying skill of the

SeaWiFS chlorophyll retrieval.

Another focus of our work this year has been the

dynamics of chromophoric dissolved organic matter (CDOM),

which influences ocean color by way of its influence on

absorption of blue and ultraviolet light, and is an important

intermediate in climate-relevant photochemical reactions.

Using global data from SeaWiFS, we have evaluated, for the

first time, the global time/space distribution of colored

dissolved organic materials or CDOM (Siegel et al., in press).

With these new data, we have shown that open ocean CDOM

distributions are not created from run-off of terrestrial

materials, but by local oceanic processes. Working with other

SIMBIOS researchers and their data sets, we also have shown

that CDOM makes the dominant contribution to blue light

absorption in the global ocean indicating that the problem of

the contribution of CDOM to ocean color is greater than had

been expected (Siegel et al. in prep).

We have continued our field and laboratory work on the

(biological) sources and (physical) sinks of CDOM in the

Sargasso Sea, with additional NSF support. Ship time from

these investigations has allowed us to make a section of inwater optical properties and CDOM from the BATS site south

to 25N (Fig. 15.4); these additional data will be contributed to

SeaBASS. Our research in this area also includes developing

a method for retrieving quantum yield spectra for

photochemical reactions from field measurements of

irradiance spectra, absorption coefficient spectra of CDOM,

and in situ photochemical production rates. This new

capability will enable us to branch out to study a large number

of global biogeochemical problems including the lightinduced cycling of carbon monoxide and dimethyl sulfide

(both important tropospheric trace gases).

REFERENCES

Mitchell, B.G., 1990: Algorithms for determining the

absorption coefficient for aquatic particles using the
SIMBIOS Project Annual Report

103

quantitative filter technique. Ocean Optics X,

Proceedings of S.P.I.E, 1302, 137-142.

O'Brien, M.C., D.W Menzies, D.A. Siegel, and R.C. Smith,

(In Press): Long-Term Calibration History of Several

Marine Environmental Radiometers (MERs). NASA

Tech. Memo, SeaWiFS Postlaunch Calibration and

Validation Analyses, Part 3, Chap. 4.

Nelson, N.B., D.A. Siegel, and A.F. Michaels, 1998: Seasonal

dynamics of colored dissolved material in the Sargasso

Sea, Deep Sea Research I, 45, 931-957.

Siegel, D.A., S. Maritorena, N. B. Nelson, D.A. Hansell and

M. Lorenzi-Kayser, (In press): Global ocean distribution

of colored dissolved and detrital materials. Journal of

Geophysical Research.

D.A. Siegel, S. Maritorena, B.G. Mitchell, N.B. Nelson, H.M.

Sosik, J.R. Morrison, K.L. Carder, M. Kahru, J. Wieland,

B. Langston. (In prep) Dominance of Colored Dissolved

Organic Material in Determining Light Availability in the

Sea. for Limnol. & Oceanogr.

Figure 15.1: Calibration history for SPMR system sn028, Irradiance head sn058, low gain.
SIMBIOS Project Annual Report

104

Figure 15.2: Comparison of average remote sensing reflectance (Lu/Ed) in the top 20m from the BBOP Biospherical MER-2040

s/n 8733 (×), and the BBSR Satlantic SPMR s/n 028 (), during July 2000. a) Using factory-supplied immersion coefficients

for SPMR s/n 028, b) using immersion coefficients for SPMR s/n 028 measured at CHORS, March 2001.

Figure 15.3: a) bottle fluorometric Chlorophyll-a samples compared to field radiometric data converted to Chlorophyll using

OC4v4 and SeaWiFS Chlorophyll-a (8-day averages). b-d) time series of LwN at 443, 490 and 555nm measured by BBOP

and SeaWiFS.
SIMBIOS Project Annual Report

105

Figure 15.4: ag (325nm), m-1 Section along the 64 40'W line from the BATS site (31 40'N) to 25 N, August 2001. This plot

shows the surface minimum and subsurface maximum of CDOM demonstrated by Nelson et al. (1998) extending across the

whole Sargasso Sea system. Also apparent are a gyre-wide minimum present in the subtropical mode water (250-500m).

TABLE 15.1: A PARTIAL LIST OF MEASUREMENTS MADE BY BBOP & BATS

BBOP Direct Measurements and Primary Derived Products :

Ed(z,λ) Downwelling vector irradiance (325, 340, 380, 412, 443, 488, 510, 555, 565, 665 & 683 nm)

Ed(0+,λ) Incident irradiance (325, 340, 380, 412, 443, 488, 510, 555, 565, 665 & 683 nm)

Lu(z,λ) Upwelling radiance (325, 340, 380, 412, 443, 488, 510, 555, 565, 665 & 683 nm)

chl-fl(z) Chlorophyll fluorescence with a WetStar fluorometer

T(z) & S(z) Temperature and conductivity with Ocean Sensors probes (calibrations by Satlantic)

atp(λ) Particulate absorption spectrum by QFT

ad(λ) Detrital particle absorption spectrum by MeOH extraction

ays(λ) Colored dissolved absorption spectrum

chl-a(z) Discrete chlorophyll a determinations via Turner fluorometry

LwN(λ) Normalized water leaving radiance (325, 340, 380, 412, 443, 488, 510, 555, 565, 665 & 683 nm)

RRS(0-,λ) In-water remote sensing reflectance (325, 340, 380, 412, 443, 488, 510, 555, 565, 665 & 683 nm)

Kd(z,λ) Attenuation coefficient for Ed(z,λ) (325, 340, 380, 412, 443, 488, 510, 555, 565, 665 & 683 nm)

Kl(z,λ) Attenuation coefficient for Lu(z,λ) (325, 340, 380, 412, 443, 488, 510, 555, 565, 665 & 683 nm)

RRS(0+,λ) Above-water remote sensing reflectance (350 to 1050 nm)

aph(λ) Phytoplankton absorption spectrum (= ap(λ) - adet(λ))

<PAR(z)> Daily mean photosynthetically available radiation at depths of the in situ C14 incubations

U.S. JGOFS BATS (NSF) and Related Biogeochemestry Sampling Programs

Primary Production (in situ 14C incubation) Sinking flux (sediment trap array)

Phytoplankton pigments (fluorometric & HPLC) Nutrients (NO3+NO2, SiO4, PO4)

CO2 system (alkalinity, TCO2 and pCO2) Continuous atmosphere & surface pCO2

Dissolved oxygen (continuous & discrete) Zooplankton biomass & grazing

POC & PON (POP infrequently) DOC & DON (DOP infrequently)

Full water column, WOCE-standard CTD profile Bacterial abundance and rates

Phytoplankton abundance by flow cytometry Coccolithophore abundance

Validation spatial cruises (5 days, 4cruises/year) Deep ocean sediment sinking rates
SIMBIOS Project Annual Report

106

This research was supported by

 the SIMBIOS NASA contract # 00200

PEER REVIEWED PUBLICATIONS

Behrenfeld, M.J., E. Marañon, D.A. Siegel and S.B. Hooker,

2001: Modeling oceanic primary production:

Photoacclimation and nutrient effects on light-saturated

photosynthesis. In press Marine Ecology Progress Series.

Dickey, T., S. Zedler, D. Frye, H. Jannasch, D. Manov, D.

Sigurdson, J. D. McNeil, L. Dobeck, X. Yu, T. Gilboy, C.

Bravo, S. C. Doney, D.A. Siegel and N. Nelson, 2001:

Physical and biogeochemical variability from hours to

years at the Bermuda Testbed Mooring: June 1994 -

March 1998. Deep Sea Research, II, 48, 2105-2140.

Mitchell, B.G. and others including N. Nelson 2000:

Determination of spectral absorption coefficients of

particles, dissolved material, and phytoplankton for

discrete water samples. In: Ocean Optics Protocols for

Satellite Ocean Color Sensor Validation, Revision 2, eds.

G.S. Fargion and J.L. Mueller, NASA TM2000209966.

Nelson, N.B. and D.A. Siegel. (In Press): Chomophoric DOM

in the Open Ocean: Biogeochemistry of Marine Dissolved

Organic Matter, Edit by D. A. Hansell and C. A. Carlson.

Nelson, N.B., N.R. Bates, D.A. Siegel, and A.F. Michaels,

2001: Spatial variability of the CO2 sink in the Sargasso

Sea. Deep-Sea Research, Part II. 48, 1801-1821.

O'Brien, M.C., D.W Menzies, D.A. Siegel, and R.C. Smith,

(In Press): Long-Term Calibration History of Several

Marine Environmental Radiometers (MERs). NASA

Tech. Memo, SeaWiFS Postlaunch Calibration and

Validation Analyses, Part 3, Chap. 4.

Siegel, DA. 2001: Oceanography - The Rossby rototiller.

Nature, 409, 576-577.

Siegel, D.A., S. Maritorena, N. B. Nelson, D.A. Hansell and

M. Lorenzi-Kayser, (In press): Global ocean distribution

of colored dissolved and detrital materials. Journal of

Geophysical Research.

Siegel, D.A., D.M. Karl and A.F. Michaels 2001:

Interpretations of biogeochemical processes from the U.S.

JGOFS Bermuda and Hawaii time series sites. Deep-Sea

Research, Part II. 48, 1403-1404.

Siegel, D.A., N.B. Nelson, M.C. O'Brien, T.K. Westberry, J.R.

Morrison, A.F. Michaels, E.A. Caporelli, J.C. Sorensen,

S.A., Garver, E.A. Brody, J. Ubante and M.A. Hammer,

2001: The Bermuda BioOptics Project: Bio-optical

modeling of primary production from space-sensible

variables. Deep Sea Research, II, 48, 1865-1896.

Siegel, D.A., M. Wang, S. Maritorena and W. Robinson.

2000: Atmospheric correction of satellite ocean color

imagery: the black pixel assumption. Applied Optics, 39,

3582-2591.

Sorensen, J.C., and D.A. Siegel 2001: Variability of the

effective quantum yield for carbon assimilation in the

Sargasso Sea. Deep-Sea Research, Part II. 48, 2005-2035.

Toole, D.A., and D.A. Siegel, (In press): Modes and

mechanisms of ocean color variability in the Santa

Barbara Channel. Journal of Geophysical Research.

SUBMITTED

Maritorena, S., D.A. Siegel and A.R. Peterson: Optimal tuning

of a semi-analytical model for global applications.

Submitted to Applied Optics.

Westberry, T.K., and D.A. Siegel: Natural fluorescence

variability in the Sargasso Sea. Submitted to Deep-Sea

Research, Part 1.

PRESENTATIONS

Armstrong, R.A., and D.A. Siegel, 2001: A large cachement

area for sediment traps is not incompatible with sharply

defined patterns of benthic deposition. Oral presentation

made at the 2001 ASLO Aquatic Sciences Meeting in

Albuquerque, NM, February 2001.

Najjar, R., O. Zafiriou, G. Cutter, R. Kieber, and N. Nelson

2001: A model of diurnal and seasonal variations of

carbon monoxide, carbonyl sulfide, and hydrogen

peroxide in Sargasso Sea surface waters. ASLO Aquatic

Sciences Meeting in Albuquerque, NM, February 2001
SIMBIOS Project Annual Report

107

Nelson, N.B. and A.J. Scott 2001: Light energy availability for

photochemistry in the Sargasso Sea on seconds to seasons

time scales. ASLO Aquatic Sciences Meeting in

Albuquerque, NM, February 2001.

Siegel, D.A., 2000: Gelbstoff, Gilvin and Chromophoric

Dissolved Organic Material: Distribution, Dynamics and

Implications. Presented at the Department of Marine

Chemistry and Geochemistry, Woods Hole

Oceanographic Institution. Woods Hole, MA., Oct. 17,

2000.

Siegel, D.A., 2001: Global distribution and dynamics of

chromophoric dissolved organic matter: Implications for

marine photochemistry. Invited presentation made at the

2001 ASLO Aquatic Sciences Meeting in Albuquerque,

NM, February 2001.

Siegel, D.A., 2001: Gelbstoff, Gilvin and Chromophoric

Dissolved Organic Material: Distribution, Dynamics and

Implications or Love That Dirty Water!!. Presented to the

Biological Sciences Department, University of Southern

California. Los Angeles, CA., Apr. 11, 2001.

Siegel, D.A., 2001: Distribution and Dynamics of

Chromophoric Dissolved Organic Material: Implications

for Marine Photochemistry. Presented at the NASA

Ocean Color Science Team Workshop. San Diego, CA.,

May 24, 2001.

Siegel, D.A., 2001: Satellite views of the North Atlantic

Spring Bloom. Presented at the U.S. JGOFS Synthesis

and Modeling Program Workshop. Woods Hole, MA.,

July 18, 2001.

Toole, D.A., D.A. Siegel and J.W. Dacey, 2001: Modeling of

upper ocean biogenic sulfur cycling in the Sargasso Sea.

Poster presented at the 2001 ASLO Aquatic Sciences

Meeting in Albuquerque, NM, February 2001.

Westberry, T.K., and D.A. Siegel, 2001: Phytoplankton

natural fluorescence in the Sargasso Sea: Prediction of

primary production rates or an index for eddy induced

nutrient fluxes. Oral presentation made at the 2001 ASLO

Aquatic Sciences Meeting in Albuquerque, NM, February

2001.
SIMBIOS Project Annual Report

108

Chapter 16

Plumes and Blooms: Modeling the Case II Waters of the Santa

Barbara Channel

D.A. Siegel, S. Maritorena and N.B. Nelson

Institute for Computational Earth System Science,University of California, Santa Barbara, California

16.1 INTRODUCTION

The goal of the Plumes and Blooms (PnB) project is to

develop, validate and apply to imagery state-of-the-art ocean

color algorithms for quantifying sediment plumes and

phytoplankton blooms for the Case II environment of the

Santa Barbara Channel. We conduct monthly to twicemonthly transect observations across the Santa Barbara

Channel to develop an algorithm development and product

validation data set. The PnB field program started in the

summer of 1996. At each of the 7 PnB stations, a complete

verification bio-geo-optical data set is collected. Included are

redundant measures of apparent optical properties (remote

sensing reflectance and diffuse attenuation spectra), as well as

in situ profiles of spectral absorption, beam attenuation and

backscattering coefficients. Water samples are analyzed for

component in vivo absorption spectra, fluorometric

chlorophyll, phytoplankton pigment (by the SDSU CHORS

laboratory), inorganic nutrient and biogenic and lithogenic

silica concentrations (Table 16.1). We are also in the process

of developing quality assurance procedures for optical data

sets to assess complimentary optical property determinations.

Our goal is to use the PnB field data set to objectively tune

semi-analytical models of ocean color for this site and apply

them using available satellite imagery (SeaWiFS and

MODIS). In support of this goal, we have also been

addressing SeaWiFS ocean color and AVHRR SST imagery

(Otero et al., 2001). We also are using the PnB data set to

address time/space variability of water masses in the Santa

Barbara Channel and its relationship to the 1997/1998 El Niño

and have been active in various outreach activities (see

below).

16.2 RESEARCH ACTIVITIES

The big issue we have had to overcome was the loss of

the R/V Ballena in November 2000. The Ballena capsized and

was lost after when a rogue wave broke over her wheelhouse

in shallow water north of Pt. Conception. No one was hurt

and it was not during a PnB cruise. We have continued the

PnB field program now leasing time on a small boat, The

Spirit. Our partnership with the Channel Islands National

Marine Sanctuary (CINMS) provides sea time on the Ballena

as a match where we provide them a research focus. The loss

of Ballena has resulted in a large uncosted expense for the

PnB project and huge hassles for the PnB staff to overcome.

Many of our operations have had to be cutback due to the

limited capability of the Spirit. For example, we could not use

our SeaBird 911+ CTD/rosette system and had to hang bottles

on wire rope. This results in reduced CTD data quality and

more work at sea. We have been assured by CINMS that a

replacement vessel will be in place by the summer of 2002.

Funds for a new research vessel are in place and we have had

extensive input in this process. Issues with the Ballena aside,

the PnB project has had a very successful field year. We have

conducted 16 one-day cruises since November 2000 and have

completed nearly 100 stations. Nearly 200 discrete samples

have been collected and HPLC samples have been sent to

SDSU for analysis. All data have been submitted successfully

to the SeaBASS system once they have been analyzed. We

have also incorporated the PnB data set in the global AOP/IOP

data set being compiled by Maritorena on his SIMBIOS

project and have provided it to Jay O'Reilly for his algorithm

work. The PnB project has also participated in two cruises on

the R/V Pt. Sur in support of the Santa Barbara Channel Long

Term Ecological Research (LTER) study

(sbc.lternet.edu/). We expect this collaboration to

continue through the lifespan of the PnB program. We have

continued our acquisition and analysis of satellite ocean color

(SeaWiFS) and thermal imagery (AVHRR) from the UCSB

HRPT ground station (HUSC). The UCSB ground station is

supported by SIMBIOS, NSF and NOAA/NESDIS. Of

particular interest are the match-ups between the PnB field

sampling and the satellite data

(www.icess.ucsb.edu/~motero/ASLO_2001_Poster.html). 

Many comparisons between SeaWiFS and field estimates

are quite good especially when assessing time/space patterns.
SIMBIOS Project Annual Report

109

However, direct comparisons of several properties are

disappointing and a significant underestimate of the SeaWiFS

estimated LwN(412) values remains. That said, the time/space

patterns are consistent between satellite and field data sets (see

below). The PnB project has an extensive education and

outreach component. We are active in local K-12 education

and have provided content for an online "Plumes and Blooms

Curriculum" assembled by the CINMS education coordinator

(see www.cinms.nos.noaa.gov/pcw2/). This curriculum is

designed for 4th through 8th graders in the Santa Barbara and

Ventura Counties. We have also participated with last

summer's Sustainable Sea Expedition program in the Santa

Barbara Channel and will with the upcoming Jason and

Project Oceanography visits. Our outreach component has

also made several presentations at several public meetings

dealing with issues as diverse as watershed processes and

coastal pollution to the California space industry.

16.3 RESEARCH RESULTS

Academic research has gone along two major fronts; 1)

the analysis of field and satellite data to assess the sources of

ocean color variability (Toole and Siegel, 2001; Otero et al.,

2001) and the assessment of the time/space variability in the

Santa Barbara Channel (Siegel et al. 2001; Shipe et al. 2001;

Mertes and Warrick, 2001). Of particular interest is our work

assessing the sources and modes of ocean color variability

within the Santa Barbara Channel from the six-year PnB

observational record (Siegel et al. 2001; Toole and Siegel,

2001). The characterization of the substances, processes, and

mechanisms that regulate coastal ocean color variability is

crucial for the application of ocean color imagery to the

management of marine resources. Using an empirical

orthogonal function analysis, we find that nearly two-thirds of

the observed variability in remote sensing reflectance is

contained in a backscattering mode. Phytoplankton absorption

makes a much smaller contribution to the observed variance in

the water-leaving radiance spectrum. Hence, particulate

backscattering associated with suspended sediment

concentrations is the dominant driver of ocean color

variability for this environment. However, sediment plumes

appear to play a much smaller role on biological processes. An

empirical partitioning of physical, biological and chemical

oceanographic parameters suggests that physical

oceanographic processes (i.e., upwelling and horizontal

advection) have the dominant role in determining

phytoplankton pigment biomass for this region. Similar

partitioning is found spatially using satellite ocean color

imagery from the SeaWiFS mission. We expect to have

several manuscripts (in addition to Toole and Siegel, 2001) on

the spatial/temporal partitioning between Plumes and Blooms

submitted over the next year.

16.4 FUTURE WORK

PnB has had a good year, especially considering the loss

of the Ballena. We plan to continue our field program as we

have in past years. From an analysis perspective, we plan to

focus on the evaluation of the inherent optical property

determinations made and their relationship with the

determinations of water-leaving radiance. We will also tune a

semi-analytical ocean color model using PnB data with the

goal of assessing differences in ocean color model tunings on

local and global scales. There are also several data quality

issues that we want to work out. These include a re-estimation

of the path length amplification factor used in the processing

of particulate absorption spectra and a convergence of

methods between the LTER and PnB projects. Satellite data

analyses are falling far behind our progress in the field

program. The major reason is that the so-called "negative

water leaving radiance problem" has not been satisfactorily

corrected in the version 3 processing of SeaWiFS imagery (see

Otero et al. 2001 on line poster). We will continue to work

with Goddard staff to help find solutions in the next

reprocessing.

REFERENCES

Mertes, L.A.K., and J.A. Warrick, 2001. Measuring flood

output from 110 coastal watersheds in California with field

measurements and SeaWiFS. Geology, 29, 659-662.

Siegel, D.A., and others, 2001: Time-series assessment of

sediment plumes and phytoplankton blooms in the Santa

Barbara Channel, California. EOS Trans., AGU, 82(47) Fall

Meet. Suppl., Abstract 0S51E-06.

Shipe, R.F., U. Passow, M.A. Brzezinski, D.A. Siegel and

A.L. Alldredge, 2001: Effects of the 1997-98 El Nino on

seasonal variations in suspended and sinking particles in the

Santa Barbara Basin. In press Progress in Oceanography.

Toole, D.A., and D.A. Siegel, 2001: Modes and mechanisms

of ocean color variability in the Santa Barbara Channel. In

press Journal of Geophysical Research.
SIMBIOS Project Annual Report

110

TABLE 16.1: PLUMES AND BLOOMS MEASUREMENTS AND DATA PRODUCTS

Direct Measurements:

Ed(z,λ) Downwelling vector irradiance (325, 340, 380, 412, 443, 490, 510, 555, 565, 665 & 683 nm)

Ed(0+,λ) Incident irradiance (325, 340, 380, 412, 443, 490, 510, 555, 565, 665, 683 & 350-1050 nm)

Lu(z,λ) Upwelling radiance (325, 340, 380, 412, 443, 490, 510, 555, 565, 665 & 683 nm)

a(z,λ) In situ absorption spectrum using WetLabs AC-9 (410,440,490,520,565,650,676 & 715 nm)

c(z,λ) In situ beam attenuation spectrum (same as above)

bb(z,λ) In situ backscattering spectrum - HOBI Hydroscat (442,470,510,532, 590 & 671 nm)

T(z) & S(z) SeaBird temperature and conductivity probes

ap(zo,λ) In vivo particulate absorption spectrum by Mitchell (1990)

adet(zo,λ) In vivo detrital particle absorption spectrum by MeOH extraction

ag(zo,λ) In vivo colored dissolved absorption spectrum

chl-a(zo) Discrete chlorophyll a determinations by Turner fluorometry

pigs(zo) Discrete phytoplankton pigment sample to be run by HPLC (SDSU CHORS analysis)

nuts(zo) Discrete inorganic nutrient concentrations (NO3, SiO4, PO4, NO2)

PSi(zo) Discrete particulate biogenic & lithogenic silica concentrations

Lsat(x,y,λ) SeaWiFS and AVHRR imagery from the HUSC ground station

Primary Derived Products:

Rrs(0-,λ) In-water remote sensing reflectance from profiling radiometry (see above)

LwN(λ) Normalized water leaving radiance calculated from RRS(0+,λ) and RRS(0-

,λ)

aph(zo,λ) In vivo phytoplankton absorption spectrum (= ap(zo,λ) - adet(zo,λ))

Kd(z,λ) Attenuation coefficient for Ed(z,λ) from profiling radiometry (also KL(z,λ))

b(z,λ) In situ total scattering spectrum (= c(z,λ) - a(z,λ))

Chl(x,y), etc. Processed SeaWiFS and AVHRR imagery

This research was supported by

the SIMBIOS NASA contract #00201

PEER REVIEWED PUBLICATIONS

Mertes, L.A.K., and J.A. Warrick, 2001: Measuring flood

output from 110 coastal watersheds in California with field

measurements and SeaWiFS, Geology, 29, 659-662.

Nelson, N.B., and D.A. Siegel, 2001: Chromophoric DOM in

the Open Ocean. In press in: Biogeochemistry of Marine

Dissolved Organic Matter, D.A. Hansell and C.A. Carlson,

eds. Academic Press.

Shipe, R.F., U. Passow, M.A. Brzezinski, D.A. Siegel and

A.L. Alldredge, 2001: Effects of the 1997-98 El Nino on

seasonal variations in suspended and sinking particles in the

Santa Barbara Basin, In press Progress in Oceanography.

Siegel, D.A., M. Wang, S.Maritorena and W. Robinson, 2000:

Atmospheric correction of satellite ocean color imagery:

The black pixel assumption. Applied Optics, 39, 3582-2591.

Siegel, D.A., S. Maritorena, N. B. Nelson, D.A. Hansell and

M. Lorenzi-Kayser, 2001: Global ocean distribution of

colored dissolved and detrital materials, In press Journal of

Geophysical Research.

Toole, D.A., and D.A. Siegel, 2001: Modes and mechanisms

of ocean color variability in the Santa Barbara Channel, In

press Journal of Geophysical Research.

Toole, D.A., D.A. Siegel, D.W. Menzies, M.J. Neumann and

R.C. Smith, 2000: Remote sensing reflectance

determinations in coastal environments: impact of
SIMBIOS Project Annual Report

111

instrumental characteristics and environmental variability.

Applied Optics, 39, 456-469.

Submitted

Maritorena, S., D.A. Siegel and A.R. Peterson, 2001: Optimal

tuning of a semi-analytical model for global applications.

In review in Applied Optics.

Siegel, D.A., and others, 2001: Time-series assessment of

sediment plumes and phytoplankton blooms in the Santa

Barbara Channel, California. Submitted to ContinentalShelf Research.

PRESENTATIONS

Otero, M.P., D.A. Siegel, and E.A. Fields, 2001: Satellite view

of plumes and blooms in the Santa Barbara Channel;

Validation and description. Poster presented at the 2001

ASLO Aquatic Sciences Meeting in Albuquerque, NM,

February 2001.

Siegel, D.A., 2001: Dispersion and the Design of Marine

Protected Areas. Seminar presented at the Institute for

Computational Earth System Science, University of

California at Santa Barbara, May 2, 2001.

Siegel, D.A., 2001: Education and Research at the University

of California or Satellite Views of Plumes and Blooms of

the Santa Barbara Channel. Presented at the Space Coast

Summit 2001 sponsored by the California Space Authority,

Santa Maria, CA, August 6, 2001.
SIMBIOS Project Annual Report

112

Chapter 17

Algorithms for Processing and Analysis of Ocean Color Satellite

Data for Coastal Case 2 Waters

Richard P. Stumpf

NOAA National Ocean Service, Silver Spring, Maryland

Robert A. Arnone and Richard W. Gould, Jr.

Naval Research Laboratory (NRL), Stennis Space Center, Mississippi

Varis Ransibrahmanakul

Technology Planning and Management Corporation, Silver Spring, Maryland

Patricia A. Tester

NOAA National Ocean Service, Beaufort, North Carolina

17.1 INTRODUCTION

The development of several new ocean color sensors has

significantly enhanced our ability to understand processes in

the open ocean. Applicability of the sensors to the coastal

zone will require additional improvements to the standard

global algorithms for both the atmospheric correction and the

bio-optical models. With the short temporal scales and fine

spatial scales of coastal processes, applying and intercomparing multiple sensors will assure their use in improving

our understanding of a range of processes and events including

algal bloom development, river plumes, and resuspension

events. SeaWiFS imagery has shown problems when applied

to the coastal zone-in particular underestimating the water

reflectance in the blue bands (412-490 nm). Two major

factors linked to atmospheric correction produce this effect:

(1) scattering from inorganic particles that produces waterleaving radiance in the near-infrared, where all radiance is

presumed to come from the atmosphere and (2) absorbing

aerosols associated with the land that cannot be incorporated

into atmospheric corrections based on near-infrared (NIR)

bands. Iterative techniques, which draw on coupling the

atmospheric and oceanic models, offer a solution to these

problems, but these require valid ocean bio-optical models for

Case 2 waters that are robust in a coupled atmosphere-ocean

model. Our objective is to develop and improve the

functionality and utility of SeaWiFS and other ocean color

satellite in Case 2 water. We are collecting data sets of biooptical properties in turbid, Case 2 waters. These data sets

will provide the basis for testing and evaluating in situ and

atmospheric correction algorithms for the coastal waters. In

coastal waters with a high sediment load or high

concentrations of colored dissolved organic matter (CDOM),

standard processing algorithms typically fail (negative or

erroneous retrievals of water-leaving radiance) due to invalid

assumptions related to the atmospheric correction. Our efforts

will focus on describing the reflectance properties at nearinfrared (NIR) and blue wavelengths in coastal waters, and

how we can utilize these properties to improve the

atmospheric correction. We will modify and develop new

coastal bio-optical algorithms, and we will validate the

algorithms and atmospheric corrections and compare the new

results with current ocean color algorithms. Finally, we will

process MODIS imagery with the new coastal algorithms and

compare the results with SeaWIFS imagery.

17.2 RESEARCH ACTIVITIES

We have made substantial progress in four areas this year,

starting in April 2001: 1) cruise and data collection, 2)

improved algorithms, 3) evaluation data sets and matchups,

and 4) MODIS software processing. We collected in situ biooptical data on nine cruises see Table 17.1.
SIMBIOS Project Annual Report

113

Table 17.1 Cruises

Experiment Cruise Date # Stations

Each Cruise

Cojet 3 –Northern Gulf of

Mexico Mopex 16-May-01 36

Cojet 3–Northern Gulf of

Mexico Ocolor1 16-May-01 22

Cojet 3–Northern Gulf of

Mexico Lgssur 16-May-01 31

Leo2001- East Coast –New

jersey R/V NorthStar 7/31- 8/02 25

11Sep01_ Biloxi- Northern Gulf

of Mexico Ocolor2 11-Sep 8

ECOHAB- West Florida Shelf ECOHAB 9/25/00-9/29/00 25

NC-04/01- North Carolina Pamlico 4/10/01-4/11/01 23

NC 07/01 North Carolina Pamlico 7/24/01 – 7/26/01 16

NC 10/01 North Carolina Pamlico 10/16/01 – 10/18/01 11

Ferry 8/01 Pamlico 5/01 4

Offshore 8/01 NC Shelf 8/1/01 2

Offshore 10/01 NC Shelf 10/19/01 3

TOTALS 12 206

Measurements in the Northern Gulf of Mexico include

absorption coefficient, beam attenuation coefficient, scattering

coefficient (ac9), remote sensing reflectance (above water

method), aerosol optical thickness (Microtops), and HPLC

pigments. A substantial number of these stations were

collected during minimum cloud cover and matched up with

SeaWIFS and MODIS passes. Measurements in North

Carolina waters include water samples, water profile

measurements (YSI), and remote sensing reflectance. The

water samples were collected to determine chlorophyll,

CDOM, filtered pad absorption, HPLC, total suspended solids

(TSS), and nutrients. The April experiment in North Carolina

co-occurred with overflights conducted by NASA-Wallops

using airborne oceanic lidar (AOL), the Satlantic SAS, and

two hyperspectral systems. We are assembling a satellite/in

situ matchup database and we will be presenting SeaWiFS

algorithm validation results at the San Francisco AGU Fall

meeting in December 2001. The HPLC filters have been

provided to CHORS and we are waiting for them to complete

the processing, at which point we will submit the pigment

results to SIMBIOS. We are in the process of submitting all

the station data to SIMBIOS. Since this is our initial effort in

data submission to SIMBIOS, we are learning the format

requirements. The data collected to date will be submitted by

November, 2001.

17.3 RESEARCH RESULTS

We have analyzed and quality-checked all the station data

collected to date and we have developed a coupled

ocean/atmosphere algorithm to improve SeaWiFS radiance

retrievals in coastal waters.

 Calibration (NOAA)

The current calibration of SeaWiFS involves two steps:

correction for temporal changes using lunar observations and

periodic vicarious calibration of the radiance based on

comparison with the Marine Optical Buoy (MOBY) sites. The

later has potential uncertainty of 0.5 % in the top-ofatmosphere (TOA) radiance calibration (Barnes et al. 2000),

partly because each band is calibrated independently. In

coastal areas, the calibration of the 412 nm band is of

particular concern owing to the need to correct for absorbing

aerosols in the atmosphere and the need to monitor and

compensate for colored dissolved organic matter (CDOM) in

the water. Calibration errors of 0.5-1% between bands in the

blue (412 nm, 443 nm, 490 nm) can introduce significant

errors in the retrieved water reflectances (5-10% in case 1

water, and 20-100% in case 2 water) .
SIMBIOS Project Annual Report

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Examination of the spectral shape in the blue shows

fundamental differences between that observed in the field and

that observed from SeaWiFS. We identified spectral shape

characteristics from the statistical patterns seen in the field

data. By adjusting the calibration of 412 nm, we found that

we could match the statistical distribution of spectral slopes in

SeaWiFS imagery to the field data (Figure 17.1).

The Kolmosgorov-Smirnov test (e.g., Siegel 1956) was

used to determine the statistical similarities between the

distributions of field and satellite data at different calibrations.

The results are presented in Figure 17.1, where 0 and 1

indicate the two distributions are alike and very much

different, respectively. Our analysis suggests the optimum

calibration at 412 nm should be 1% higher than the current.

The analysis gives the same results for different atmospheric

corrections in both Case 1 and Case 2 waters (although Case 2

waters provide a sharper separation), showing robustness, and

does not use or require simultaneous field data. For

validation, we computed the overall bias of 159 pairs of

satellite and same-day measured remote sensing reflectance in

US coastal waters for the existing calibration (thin line, Figure

17.2) and the new calibration (bold line, Figure 17.2). The

new 412 calibration reduced most of the relative bias at 412

nm.

 The analysis gives the same results for different

atmospheric corrections in both case 1 and case 2 waters

(although case 2 waters provide a sharper separation), showing

robustness, and does not use or require simultaneous field

data. For validation, the overall bias of 159 pairs of satellite

and same-day measured remote sensing reflectance in US

coastal waters was examined for the existing calibration and

the new calibration (Figure 17.2). The new 412 calibration

reduced most of the relative bias at 412 nm

Validation of Atmospheric Correction and Chlorophyll

Algorithms for Processing SeaWiFS data (NOAA)

We have developed an evaluation protocol to evaluate the

performance of the available atmospheric corrections

(including the NOAA/NRL developmental atmospheric

correction) and chlorophyll algorithms. To date, we have used

159 same-day field-satellite pairs of remote sensing

reflectance spectra to determine the best atmospheric

correction applicable to the entire US coastline. The five

atmospheric corrections considered were developed by

Gordon & Wang (1994), Siegel et al. (2000), Gould et al.

(1998), Ruddick et al. (2000), and NOAA/NRL (Stumpf et al.,

in prep). We did not include the atmospheric correction

developed by Hu et al. (2000) because it requires manual

interaction and is not appropriate for automated processing.

Similarly, we have 124 same-day field-satellite pairs of

chlorophyll to determine the most appropriate regional

chlorophyll algorithms. The chlorophyll algorithms

considered were OC2V2 (O'Reilly), OC4V4 (O'Reilly et al.

1998 ), USF-analytical (Carder et al. 1999), CALP6 (Kahru

and Mitchell 1999), and NOAA-coastal (Stumpf et al., 2000).

The field measurements were obtained from NASA SIMBIOS

database and NOAA collaborators (Nan Walker- LSU, Dana

Woodruf-Battelle Marine Sciences Laboratory, Linda

Traykovski-Woods Hole). In both the evaluations of

atmospheric correction and chlorophyll algorithms, the

selection procedure was designed to determine an algorithm

that works best over a range of water types and compensates

for distribution biases.

Preliminary results are encouraging to the developing

algorithms. The results indicate a NOAA atmospheric

correction estimated the best overall accurate remote sensing

reflectance (Stumpf et al. in prep). The best chlorophyll

algorithms vary by regions: NOAA-coastal algorithm for US

Gulf of Mexico and Southeast, and central Gulf of Maine;

NASA OC2V2, for California water (Ransibrahmanakul, in

prep).

In addition to comparing the performances of the existing

algorithms, the work to develop better chlorophyll algorithm is

ongoing. In Pamlico Sound and Atchafalaya Bay, the NASA

OC2V2 or OC4 appear to perform better than NOAA-coastal

algorithm. We will be examining the factors influencing this,

high CDOM may play a factor.

New Algorithms (NRL)

We modified the MSl12 NASA SeaDAS code to include

the NIR atmospheric correction method. We are testing and

evaluating algorithms from coastal waters through a parallel

software development with NASA SeaDAS software team.

We have extracted the MS112 from SeaDAS so that it can be

operated in stand-alone mode, and we have incorporated it

into the NRL Automated Processing System (APS). Outputs

from APS are directly compatible with SeaDAS except we

have added routines to handle the new NRL products

(presently a total of 65 products). The APS HDF-formatted

products can be directly incorporated into SeaDAS, to employ

all the functionally of the display and data manipulation

routines. In addition, we can easily apply the changes in

calibration that we routinely receive from the SeaDAS

program. APS can produce “identical results” as the NASA

standard processing and it can produce additional products

that we use for comparison and research. We receive all

NASA changes to SeaDAS MS112 and we incorporate these

modifications into APS, so that we are consistent with

standard NASA processing. APS has undergone several

stages of development and upgrades continue. We have a beta

2.5 version that tests new algorithms and can handle MODIS

processing (discussed later.)

Algorithm modifications in APS v2.5 fall into two broad

categories: 1) changes to the atmospheric correction, and 2)
SIMBIOS Project Annual Report

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changes to the in-water bio-optical algorithms. Changes to the

atmospheric correction include ability to drive a coupled

atmosphere/ocean optical property model (we call this the NIR

correction). This algorithm uses the NIR changes to estimate

the water leaving radiance at 765 and 865 prior to the standard

(Gordon and Wang) atmospheric correction approach. These

modifications are applied to all coastal waters where problems

with over-correction occur. The second category includes

changes to bio-optical algorithms to estimate the total spectral

absorption coefficient (a), absorption due to phytoplankton

(aφ), absorption due to detritus and CDOM (adg), and the

backscattering coefficient. We included semi-analytical

MODIS algorithms from Carder et al. (1999) and semianalytical algorithms of Arnone et al. (2001). There are

relationships between inherent optical properties (IOPs) and

remote sensing reflectance, chlorophyll, and CDOM that are

used in both these categories of algorithm development (1 and

2). In order to ensure that the same relationships are used

throughout the software, we have assembled the bio-optical

equations into isolated software modules that are called

repeatedly during different stages of the processing. The

algorithms that are used in processing select from these biooptical modules. This permits self-consistent evaluations of

the relationships and provides an easy method to test new

relationships.

We developed improved SeaWIFS coastal ocean color

algorithms to derived inherent optical properties, based on

relationships between absorption, scattering and remote

sensing reflectance. The linear remote sensing reflectance to

scattering: absorption ratio (bb/a) is the basis for open ocean

algorithms where absorption (predominantly from

chlorophyll) is greater than backscattering. In coastal waters,

where backscattering from sediment can dominate absorption,

the relationships of bb/(a+bb) and a non-linear and spectral

dependence occurs between the reflectance and the

backscatter: absorption ratio.

 These nonlinear influences (Haltrin, 2001) affect not only

the in water optical algorithms, but they are also coupled with

the atmospheric correction in coastal waters. The removal of

water leaving radiance in the near-IR (765 and 865 nm) is

especially necessary in coastal waters. The non-linear

relationship is used in estimating the water leaving radiance in

the near-IR through an iterative pixel-by-pixel process using

the 670 nm water leaving radiance.

We used SeaWIFS imagery and insitu measurements to

evaluate the effects the non-linear relationships have on

coastal algorithms where backscattering dominates the

absorption. We compare these new products with the more

standard NASA products and we highlight areas where

regional differences are greatest (bays, estuaries).

17.4 CONCLUSION

 Evaluation of algorithms / bio-optical algorithms

(NOAA/NRL)

We are evaluating and validating the algorithms and

retrieved optical properties using in situ coastal bio-optical

measurements collected from 1997- present (lifespan of

SeaWiFS). This effort is designed to develop an architecture

for 1) handling multiple cruises and satellite/in situ data

matchups, and 2) to provide guidance on algorithm

development, testing, and validation. Our focus has been

mostly on coastal values, however there are data from offshore

waters included as well (such as the Japan/East Sea).

Between the two groups, NOAA and NRL, we have an

extensive data set. We are in the process of merging the

information for examination of remote sensing reflectance and

chlorophyll. NRL has a data set from over 18 cruises,

representing 76 separate cruise days, with a total of

approximately 150 stations with chlorophyll data, and a total

of 330 stations with remote sensing reflectance measurements.

Note that these are total station numbers; the actual number of

matchups will be slightly lower after removal of contaminated

SeaWiFS pixels (due to clouds, swath edge). NOAA has

approximately 100 same-day match-ups of remote sensing

reflectance and chlorophyll from the southeast and Gulf of

Mexico. We assembled a database of in situ optical properties

to evaluate SeaWIFS-derived properties collected from 1997

to present in a variety of coastal regions (Mississippi Bight,

Mississippi River, West Florida Shelf, Loop Current, North

Carolina, New Jersey). The data cover a broad range of

absorption (0.4 – 15 m-1) and scattering (0.1 – 27 m-1)

coefficients and remote sensing reflectance.

Optical properties derived from SeaWIFS were evaluated

for 18 cruises in coastal and open ocean waters in the Gulf of

Mexico, US East Coast, and the Japan/ East Sea. The inherent

optical properties (absorption and scattering) were derived

from SeaWIFS processing using a NIR correction with a

coupled ocean- atmosphere algorithm. Coastal optical

properties are more complex and differ greatly from

chlorophyll – dominated open-ocean waters. The coupled

algorithms improved results and extend SeaWIFS optical

properties well into bays and estuaries where high sediments

and CDOM absorption dominate the optical signature.

 The in situ data were used to estimate the error associated

with in-water optical algorithms based on remote sensing

reflectance. We then estimated the error associated with

satellite optical products for a variety of different optical

regimes (CDOM rich, sediment rich, and chlorophyll rich).

As a whole, we noted higher error associated with coastal

waters than open ocean.
SIMBIOS Project Annual Report

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With the high CDOM patterns found in the region, we are

examining the impact of CDOM on the determination of

chlorophyll, as well as on the atmospheric correction.

Sensor inter-comparison: MODIS & SeaWIFS (NRL)

We are developing and implementing similar processing

algorithms for MODIS that we are using for SeaWiFS. This

enables us to evaluate and compare products and algorithms

between two sensors. We have implemented MODIS software

obtained from the University of Miami into APS 2.5; this is

the “standard” processing that is being used by the MODIS –

Oceans Science team (Gordon atmospheric correction). To

date, we have:

• Obtained MODIS source code for modcolor subroutine

from Miami (RATFOR source).

• Modified modcolor for input into APS (NRL-HDF) v2.5

which is a C file with 2 entry points in main loop of

Miami Code (anly8dbl.rat).

• Developed make files based on GNU automaker/

autoconf for modcolor.

• Compiled MODIS code on Linux and SGI – Required

Portland Groups f90 compiler (Linux).

• Applied APS imgmap program for warping /registration

based on PC-SeaPAC.

• Compared the MODIS products from DACC with

products we are producing in APS.

• Developed procedures to automatically match input file

with correct ancillary data.

• Developed procedures to automatically place regional

images into data bases on web server.

• Processed ~40 MODIS scenes and corresponding

SeaWIFS scenes and produced bio-optical products.

• Produced HDF MODIS files currently viewed under

SeaDAS (NRL) for 12 products which include Rrs (l),

k532, chlmod, chl_carder, adg400_card, aph_card,

chloc3, chl0c4, albedo865, tau865,l2flags, glint, foam, sst

We have identified several issues that require further work:

• Access to ancillary fields - requires met/ozone AND

Reynolds OI/ SST . The met/ozone are coming from

DAAC (SeaWIFS) and the Reynolds data come from

NOAA. The processing requires real-time data and will

not work with the climatology files (unlike anly8db1 for

SeaWIFS). We are planning to use the SeaDAS Code for

processing the met/ozone data directly.

• Limits on the file size (5-minute scene) and non

partitioning of file size (lines vs. samples).

• Problems with in-water algorithms - are they real and

systematic ?

• Issues with level 0 to level 1 - conversion is time

dependent and a product of the MCST group.

We have shown our current MODIS results to Wayne

Esaias in August 2001. Bio-optical products from SeaWIFS

and MODIS (Terra) were compared from coastal and open

ocean waters in the Gulf of Mexico and the US East Coast.

Near coincident overpasses of these satellites (within 1 hour)

were processed with similar methods to determine the inherent

optical properties (absorption and scattering), diffuse

attenuation coefficient (k) and the chlorophyll concentration.

The similar spectral and spatial resolution and repeat cycles of

these sensors enable us to assess temporal variability in coastal

processes. We evaluate the satellite bio-optical products using

coincident shipboard measurements and we assess the

variability and regional dependence of the algorithms.

Processing of MODIS and SeaWIFS imagery was performed

on modified University of Miami (modcol) software on a

LINUX operating system. We compared the “standard”

processing code for these satellites with NIR atmospheric

correction methods and determined the differences in optical

properties within the coastal zone. The MODIS predicted

coastal bio-optics were elevated relative to the SeaWIFS and

shipboard values and were linked to the NIR correction. We

noted largest differences in the high sediment estuarine

regions and river discharge regions and smaller differences

offshore (Figure 17.3). Because of the importance of

atmospheric correction in bio-optical estimates, we examined

the effects due to differences in the aerosol models and optical

depths that were used by the two satellites (Figure 16.4).

17.5 FUTURE WORK

We anticipate the following cruises:

Nov 2001 – Northern Gulf of Mexico

March 2002 – Northern Gulf of Mexico 

May 2002 – Northern Gulf of Mexico

Feb, April, 2002 – North Carolina

July, Oct. 2002 – North Carolina

In addition, we will be continuing development on the

several fronts: bio-optical algorithms for chlorophyll, algal

bloom, CDOM, and scattering; atmospheric correction and

calibration required for coastal areas; and implementation with

MODIS.

REFERENCES

Barnes, R.A., R.E. Eplee Jr., W.D. Robinson, G.M., Schmidt,

F.S. Patt, S.W. Bailey, M. Wang, and C.R McClain, 2000:

The calibration of SeaWiFS on orbit, Earth Observing
SIMBIOS Project Annual Report

117

Systems V, William L. Barnes (eds.), Proceedings of

SPIE Vol. 4135, 281-293.

Carder, K.L., F.R. Chen, Z.P. Lee, S.K. Hawes, D.

Kamykowski, 1999: Semianlytical moderate resolution

imaging spectrometer algorithms for chlorophyll a and

absorption with bio-optical domains based on nitratedepletion temperatures, Journal of Geophysical Research,

104, 5403-5421.

Fu, G., K.S. Baith, and C.R. McClain, 1998: SEADAS: the

SeaWiFS Data Analysis System, Proceedings of the 4th

Pacific Ocean Remote Sensing Conference, Qingdao,

China, July 28-31.

Gordon, H.R., and M. Wang, 1994: Retrieval of water leaving

radiance and aerosol optical thickness over the oceans

with SeaWiFS: a preliminary algorithm, Applied Optics,

33, 443-452.

Gould, R.W., Jr., R.A. Arnone, M. Sydor, 1998: Testing a new

remote sensing algorithm to estimate absorption and

scattering in CASE 2 waters, SPIE Ocean Optics XII,

Hawaii, Nov, 1998.

Hu, Chuanmin, K.L. Carder, F.E. Muller-Karger, 2000:

Atmospheric correction of SeaWiFS imagery over turbid

coastal waters: a practical method, Remote Sensing of

Environment, 74, 195-206.

Kahru, K., and G.B. Mitchell, 1999: Empirical chlorophyll

algorithm and preliminary SeaWiFS validation for the

California current, International Journal of Remote

Sensing, 20, 34323-3429.

O'Reilly, J.E., S. Maritorena, G.B. Mitchell, D.A. Siegel, K.L.

Carder, S.A. Garver, M. Kahru, and C. McClain, 1998:

Ocean color chlorophyll algorithms for SeaWiFS, Journal

of Geophysical Research, 103, 24937-24953.

Ruddick, K., F. Ovidio, and M. Rijkeoboer, 2000:

Atmospheric correction of SeaWiFS imagery for turbid

coastal and inland waters, Applied Optics, 39, 897-912.

Siegel, D., M. Wang, S. Maritorena, and W. Robinson 2000:

Atmospheric correction of satellite ocean color imagery:

the black pixel assumption, Applied Optics, 39, 3582-

3591.

Sigel, S., 1956: Nonparametric statistics for the behavioral

sciences. McGraw-Hill Book Company, New York.

Stumpf, R.P. Arnone, R.A. R.W. Gould, P. Martinolich, V.

Ransiabrahmanakul, P.A. Tester, R.G. Steward, A.

Subramaniam, M. Culver, J.R. Pennock, 2000: SeaWiFS

ocean color data for US Southeast coastal waters. Sixth

International Conference on Remote Sensing for Marine

and Coastal Environments. Charleston SC. Veridian

ERIM Intl. Ann Arbor MI, USA, p. 25-27.

This research was supported by

the SIMBIOS NASA interagency agreement # S-44796-X

PEER REVIEWED PUBLICATIONS

Arnone, R.A. and R.W. Gould, 2001: Mapping Coastal

Processes with Optical Signatures, Backscatter, 12, 17-

24.

Gould, R.W., Jr., R.A. Arnone, and M. Sydor, 2001:

Absorption, scattering, and particle size relationships in

coastal waters: Testing a new reflectance algorithm,

Journal of Coastal Research, 17, 328-341.

Gould, R.W., Jr., and R.A. Arnone: Temporal and spatial

variability of satellite sea surface temperature and ocean

color. International Journal of Remote Sensing,

submitted.

Haltrin, V.I., Semi-empirical approach to the radiative transfer

of light in open and coastal sea waters, Applied Optics,

submitted.

PRESENTATIONS

Arnone, R.A., R.W. Gould, Jr., C.O. Chan, and S.D. Ladner,

2000: Uncoupling CDOM, scattering and chlorophyll

properties in coastal waters using SeaWiFS ocean color.

Abstract Published to Oceans from Space 2000, Venice,

Italy, 9-13 October, 2000.

Arnone, R.A., R.W. Gould, Jr., A.D. Weidemann, S.C.

Gallegos, and V.I. Haltrin, 2000: Using SeaWiFS ocean
SIMBIOS Project Annual Report

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color absorption, backscattering properties to discriminate

coastal waters. Abstract published Ocean Optics XV,

Monaco, 16-20 October, 2000.

Gould, R.W., Jr., R.A. Arnone, and C.O. Chan, 2000:

Temporal and spatial variability of satellite sea surface

temperature and ocean color. Oceans from Space 2000,

Venice, Italy, 9-13 October, 2000.

Gould, R.W. Jr., R.A. Arnone, C.M. Lee, and B.H. Jones,

2000: Characterizing surface and subsurface thermal and

bio-optical fields in the Japan/East Sea during a spring

bloom: Shipboard measurements and satellite imagery.

Proceedings, Ocean Optics XV, Monaco, 16-20 October,

2000.

Gould, R.W., Jr., T.R. Keen, R.A. Arnone, R.H. Stavn, A.-R.

Diercks, 2001: Bio-Optical variability in a barrier-island

coastal environment: Coupling remote sensing imagery,

shipboard measurements, and modeling results. Ocean

Odyssey 2001 Meeting, Joint meeting of IAPSO and

IABO, Mar del Plata, Argentina, 21-28 October, 2001.

Ransibrahmanakul, V., R.P. Stumpf, A. Robertson, K. Casey,

and K. Buja, 2001: Temporal patterns in chlorophyll and

temperature around the coastal US, from September 1997

through 2001. 16th Biennial Conference of the Estuarine

Research Federation, St. Pete Beach, Fl, November 4-8,

2001.

Stumpf, R.P. and M.E. Culver: Forecasting harmful algal

blooms in the Gulf of Mexico, 2001: 16th Biennial

Conference of the Estuarine Research Federation, St. Pete

Beach, Fl, November 4-8, 2001.

Tester, P, S. Varnam, R. Swift, R.P. Stumpf, D. Eslinger, M.

Culver, M. Black, 2001: Hurricane Floyd: NOAA,

NASA and new remote sensing technologies. 16th

Biennial Conference of the Estuarine Research

Federation, St. Pete Beach, Fl, November 4-8, 2001.

Figure 17.1: By adjusting calibration of 412 nm, we found that we could match the statistical distribution of spectral slopes in

SeaWiFS imagery to the field data. The horizontal axis is the different multipliers to the current calibration of 412 nm (e.g.

1.00 means 1.00 x current calibration; and 1.01 = 1.01 x current calibration, etc). The Kolmosgorov –Smirnov test (Siegel

1956) was used to compare the difference between the accumulative histograms of field and satellite data; a K-S Dmax of 0

indicates the two histograms are identical and 1 indicates the two histograms are significantly different.
SIMBIOS Project Annual Report

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Figure 17.2: For validation, the overall bias of 159 pairs of satellite and same-day measured remote sensing reflectance in US

coastal waters was examined for the existing calibration (thin) and the new calibration (bold). The new 412 calibration reduced

most of the relative bias at 412 nm.

SeaWiFS 07/31/01 1753 GMT MODIS 07/31/01 1623 GMT

Figure 17.3: Bio-optical products from SeaWIFS and MODIS (Terra) were compared from coastal and open ocean waters in

the Gulf of Mexico and the US East Coast (Diffuse Attenuation Coefficient at 532 (k532) . The largest differences occur in

the high sediment estuarine regions and river discharge regions and smaller differences offshore.
SIMBIOS Project Annual Report

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Figure 17.4: Examination of the effects due to differences in the aerosol models and optical depths that were used by

 the two satellites.

SeaWIFS k532

MODIS

 k532

1:1 Higher

MODIS
SIMBIOS Project Annual Report

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Chapter 18

Factors Affecting, and Impact of, Diazotrophic Microorganisms in

the Western Equatorial Atlantic

Ajit Subramaniam

University of Maryland, College Park, Maryland

18.1 INTRODUCTION

The tropical Atlantic Ocean is a narrow basin surrounded

by the largest and second largest river drainage systems in the

world. It is characterized by substantial upwelling of

subthermocline water rich in nutrients along its east coasts and

the equator. Nutrients inputs from the riverine discharges and

upwelling is further enhanced by the near chronic addition of

Iron through dust blown from the Sahara and sub-Saharan

Africa. The biological responses to these various forcings are

clearly visible in ocean color satellite imagery as enhanced

chlorophyll. However, there are few sea truth measurements

that allow us to distinguish between real biological response

and false positives due to the presence of dissolved organic

matter transported by the rivers or the failure of atmospheric

correction algorithms due to the presence of dust in the

atmosphere.

Dove-tailing with two National Science Foundation

funded studies – “Factors Affecting, and Impact of,

Diazotrophic Microorganisms in the Western Equatorial

Atlantic” and “Oceanic N2 Fixation and Global Climate” – we

are conducting field studies in the Atlantic and Pacific Oceans.

The main objectives of our SIMBIOS funded project are to 1)

validate ocean color satellite products using field

measurements made under dusty skies and in the Amazon

River plume, and 2) develop a semi-analytical Trichodesmium

product. In this report, we present our activities in 2001.

18.2 RESEARCH ACTIVITIES AND

RESULTS

We undertook two field surveys in the tropical and

subtropical Atlantic Ocean – JAN01SJ in January/February

2001 and JUN01KN in June/August 2001. The JAN01SJ

deployment lasted 47 days when 51 stations were occupied.

Bio-optical profiles using a spectroradiometer, an AC9, a

Hydroscat-6 and a CTD were made at 25 stations. Water

samples taken for measuring particulate absorption, dissolved

absorption, fluorometric chlorophyll and HPLC pigments at

six depths at these 25 stations. The JUN01KN deployment

lasted 55 days and 55 stations were occupied. Bio-optical

profiles using a spectroradiometer, an AC-9 and a CTD were

taken at 43 stations. The Bio-optical measurements are

described in detail below. A SIMBAD and a microtops

sunphotometer were used on clear days on both cruises (12

microtops and 9 SIMBAD measurements in the JAN01SJ

cruise and 18 microtops and 12 SIMBAD measurements on

the JUN01KN cruise). A Brookhaven National Laboratory

Shadowband radiometer was deployed on the JUN01KN

cruise and about 50 days of data was collected.

The Satlantic SPMR free-falling spectroradiometer (13

channels at 340, 3880, 412, 443, 490, 510, 520, 555, 565, 620,

665, 670, 683 nm) was fished at least 20m away from the side

of the ship and the matched surface reference sensor was

floated 20m away from the ship. The slope and intercept of a

linear regression to the natural log of the radiance data from

the top 10m measured by the profiler was used to calculate the

upwelling diffuse attenuation coefficient (Kuλ), and the

upwelling radiance at the surface. Kuλ from the profiler is

also used to extrapolate the upwelling radiance from the

surface reference from 0.75 cm to through the surface. The

above surface downwelling irradiance measured by the surface

reference is used to calculate two estimates of remote sensing

reflectance and normalized water leaving radiance using the

two estimate of water leaving radiance (Lw). A SIMBAD was

used on clear days to make above water radiance

measurements following the SIMBIOS protocols Frouin

(2000). After one dark scan, the SIMBAD is pointed directly

at the sun for 3 scans. The instrument is then pointed at the

water 45° from nadir and 135° from the sun's azimuth for 3

more scans, followed by 3 scans of the sun and one dark scan.

The water-leaving radiance data from the three methods will

be compared to each other and to the satellite-derived

products.

Total absorption and attenuation profiles were made using

an AC9 and following the protocols detailed by Twardowski

et al. (1999). The AC9 was mounted in an optics cage along
SIMBIOS Project Annual Report

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with a Hydroscat (in the JAN01SJ cruise alone) and a Seabird

CTD equipped with a Wetstar fluorometer. The optics cage

was lowered at a rate of about 5m/minute in the water column

to about 70m and then brought up, thus provide downcast and

upcast profiles of total spectral absorption and attenuation at 9

wavelengths, spectral backscattering at 6 wavelengths,

temperature, conductivity, and chlorophyll fluorescence.

Water was collected from six depths for phycoerythrin,

fluorometric chlorophyll a (Trees et al. 2000), and HPLC

pigment analysis and for particulate and dissolved absorption.

The particulate and dissolved fractions of absorption were

measured spectrophotometrically following Mitchell et al.

(2000). Between 0.5 and 3L (depending on water type) of

seawater was filtered through a GF/F filter and the absorption

spectrum of the filter was measured for total particulate

absorption using a Shimadzu UVPC2401 spectrophotometer.

After measuring total absorption due to particulates, the filter

was soaked in methanol (to remove polar soluble pigments)

the absorption due to detritus was measured. Absorption due

to the dissolved fraction was measured using a 10-cm path

length cuvette filled with 0.2 μm filtered seawater.

We have compiled a database of all the

spectroradiometric remote sensing reflectance measurements

made during cruises in the last three years (APR98RR,

NOV99EW, JAN01SJ) where Trichodesmium counts was

greater than about 1000 trichomes/L. We are working with

Dr. Siegel and others at UC Santa Barbara to develop a semianalytical model for detection of Trichodesmium. We have

processed all the SeaWiFS imagery that corresponds to the

field deployments (Figure 18.1) and are in the process of

matching the field measurements to the satellite derived

products.

18.3 FUTURE WORK

While much of the data described above has been

processed, quality controlled and submitted to SeaBASS, due

to the extremely long field deployments this year, we are still

processing some data. We will complete match ups with both

SeaWiFS and MODIS derived products. We plan to

participate in one short (~3 week) deployment in the tropical

Atlantic Ocean, four short (~4 days each) deployments in

Massachusetts Bay and one longer (45 days) deployment in

the Pacific Ocean in 2002. We also hope to make significant

progress in improving the Trichodesmium product.

REFERENCES

Frouin, R., B. Holben, M. Miller, C. Pietras, E. Ainsworth, J.

Porter and K. Voss, 2000: Sun photometer and sky

radiance measurements and data analysis protocols.

Ocean Optics Protocols for Satellite Ocean Color Sensor

Validation, Revision 2. G. S. F. a. J. L. Mueller.

Greenbelt, MD, NASA GSFC. 2000-209966: 108-124.

Mitchell, G. B., A. Bricaud, K. Carder, J. Cleveland, G.

Ferrari, R. Gould, M. Kahru, M. Kishino, H. Maske, T.

Moisan, L. Moore, N. Nelson, D. Phinney, R. Reynolds,

H. Sosik, D. Stramski, S. Tassan, C. C. Trees, A.

Weidemann, J. Wieland and A. Vodacek, 2000:

Determination of spectral absorption coefficients of

particles, dissolved material, and phytoplankton for

discrete water samples. Ocean Optics Protocols for

Satellite Ocean Color Sensor Validation, Revision 2. G. S.

Fargion and J. L. Mueller. Greenbelt, MD, NASA GSFC.

2000-209966: 125-153.

Trees, C. C., R. Bidigare, D. M. Karl and L. V. Heukelem,

2000: Fluorometric chlorophyll a: Sampling, laboratory

methods, and data analysis protocols. Ocean Optics

Protocols for Satellite Ocean Color Sensor Validation,

Revision 2. G. S. Fargion and J. L. Mueller. Greenbelt,

MD, NASA GSFC. 2000-209966: 162-169.

Twardowski, M. S., J. M. Sullivan, et al., 1999: Microscale

quantification of the absorption by dissolved and

particulate material in coastal waters with an AC-9.

Journal of Atmos. Oceanic Tech 16: 691-707.
SIMBIOS Project Annual Report

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0.02

0.10

1.00

10.00

32.00

0.04

0.10

1.00

10.00

32.00

0.04

0.10

1.00

10.00

32.00

0.04

0.10

1.00

10.00

32.00

0.04

0.10

1.00

10.00

32.00

0.04

0.10

1.00

10.00

66.83

0.02

0.10

1.00

10.00

32.00

0.03

0.10

1.00

10.00

32.00

0.04

0.10

1.00

10.00

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0.04

0.10

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32.00

A. 25 January - 1 February 2001 B. 2 February - 9 February 2001

C.10 February - 17 February 2001 D. 18 February - 25 February 2001

G. 4 July - 12 July 2001

H. 13 July - 19 July 2001 I. 20 July - 27 July 2001 J. 28 July - 4 August 2001

K. 5 August - 12 August 2001 L. 13 August - 20 August 2001

SeaWiFS 8-Day Composite Imagery of 

Chlorophyll Concentration in the 

WEQAT Region during the field surveys

in January/February and July/August.

The cruise track is shown in white with

the stations occupied for each period 

shown in grey. The large areas of 

missing data in January/February are

due to dust events leading to the failure 

of the atmospheric correction 

algorithms. Surface chlorophyll 

concentrations tend to be low. In 

contrast the chlorophyll concentrations

are very high in July/August due to

tremendous blooms of diatoms, much of

them with diazotrophic symbiotic 

cyanobacteria. The Amazon River

retroflection can also be clearly seen. 

The region is extremely dynamic with 

the Amazon River plume changing 

direction from heading southeastward

to eastward in just a few days. The 

formation of a North Brazil Current Ring

can also be seen (Figures I, J & K).

0.03

0.10

1.00

10.00

32.00

0.04

0.10

1.00

10.00

32.00

E. 18 June - 25 June 2001 F. 26 June - 3 July 2001

M. 21 August -28 August 2001

Figure 18.1: SeaWiFS 8-day compositive imagery of clorophyll concentration
SIMBIOS Project Annual Report

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This research was supported by

the SIMBIOS NASA contract #00192

PEER REVIEWED PUBLICATIONS

Subramaniam, A., C. W. Brown, R. R. Hood, E. J. Carpenter,

and D. G. Capone, 2001: Detecting Trichodesmium

Blooms in SeaWiFS Imagery. Deep-Sea Research Part II

Vol. 49/1-3 Pp 107-121.

Hood, R. R., A. Subramaniam, L.R. May, E.J. Carpenter, D.G.

Capone, 2001: Remote Estimation of Nitrogen Fixation

by Trichodesmium. Deep-Sea Research Part II Vol. 49/1-

3 Pp 123-147.

Sañudo-Wilhelmy, S.A., Kustka, A.B., Gobler, C.J.,

Hutchins, D.A.,Yang, M, Lwiza, K., Burns, J., Capone,

D.G., Raven, J.A. and Carpenter, E.J., 2001: Phosphorus

limitation of nitrogen fixation by Trichodesmium in the

central Atlantic Ocean. Nature 411: 66-69.

Capone, D.G., 2001: Marine N2 fixation: What’s the fuss?

Current Opinions in Microbiology 4: 341-348.

Michaels, A.F., D.M. Karl and D.G. Capone, in press:

Element stoichiometry, new production and nitrogen

fixation. Oceanography.

Karl, D.M., A. Michaels, B. Bergman, D.G. Capone, E.J.

Carpenter, R. Letelier, F. Lipschultz, H. Paerl, D. Sigman

and L. Stal, in press: Dinitrogen fixation in the world’s

oceans. Biogeochemistry.

PRESENTATIONS

Subramaniam, A., 2001: The PIRANA Paradigm. BioScience

Day, University of Maryland. November 2001.

Carpenter et al., 2001: Potential Influences of Riverine and

Aeolian inputs on Nitrogen Fixation in the Atlantic.

Biocomplexity Awardee Conference, National Science

Foundation, Washington DC, October 2001.

Capone, D.G., 2001: Nitrogen fixation in the world’s oceans.

PICES meeting, Victoria, October 2001.
SIMBIOS Project Annual Report

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Chapter 19

HPLC Pigment Measurements For Algorithm Development and

Validation in Support of the SIMBIOS Science Team

Chuck Trees and Jason Perl

Center for Hydro-Optics and Remote Sensing, San Diego State University, California

19.1 INTRODUCTION

Remotely sensed ocean color is determined by the

absorption and scattering properties of dissolved organic

material and suspended particulates. In most oceanic and

many coastal areas, phytoplankton, with their associated

suite of pigments, dominate the optical signal viewed by

satellite sensors. These pigments have distinct spectral

signatures, and therefore changes in the pigment-algal

assemblage result in spectral shifts in absorption and

reflectance. Therefore, accurate measurements of

phytoplankton pigments are essential in understanding

global carbon cycles in the ocean by minimizing satellite

retrieval uncertainties when mapping these properties at

regional to global scales.

The focus of this program is to characterize

phytoplankton pigments in the water column during

NASA’s Sensor Intercomparison and Merger for

Biological and Interdisciplinary Oceanic Studies

(SIMBIOS) Project. This research is to provide high

performance liquid chromatography (HPLC) pigment

analysis on samples collected by the SIMBIOS Science

Team for bio-optical algorithm validation and

development. This effort will provide the SIMBIOS

Project with an internally consistent pigment database of

the highest quality to evaluate ocean color products.

19.2 RESEARCH ACTIVITIES

In November 2000, we received the new Thermo

Quest Corporation's HPLC system, which was purchased

by NASA, and included quaternary gradient pump (Model

4000), membrane degasser (Model SCM 1000E-010),

autosampler with cooling and sample preparation options

(Model AS3000-026), fluorometer (Model FL3000-140),

photodiode array absorption detector (Model UV6000LP),

Dell computer (Model GX110), DeskJet color printer

(Model 600C), and HPLC software upgrades. In Jan 2001,

a Thermo-Quest technician came to CHORS and setup the

HPLC system, as well as installing the new software. Also

during this period, we interviewed several applicants for

the position of HPLC Analyst. We hired Mr. Jason Perl,

who had worked for Dr. Ralf Goericke, SIO, for 3.5 years

analyzing pigment samples by HPLC methods. Mr. Perl

started work at CHORS on the 7th of Mar 2001 and has

proved to be a valueable asset to the SIMBIOS Project.

During the beginning of this year, the PI participated in the

SIMBIOS Science Team Meeting and presented a review

of pigment protocols to the various PI's collecting samples

which included sampling, labeling, storage, and shipping.

During the first year of this contract, we started

receiving pigment samples from other SIMBIOS PI’s

during the last week of April 2001. Because of the delay

in receiving these samples, we were only able to process

1,362 pigment samples. The number of samples, analysis

period and delivery date to the PI are shown in Table 1. In

order to process these pigment samples, numerous pigment

calibrations were performed. Nineteen pigment standards

were purchased from DHI Water and Environment in

Denmark which included the following; chlorophyll c2,

chlorophyll c3, peridinin, 19' butanoyloxyfucoxanthin,

fucoxanthin, 19' hexanoyloxyfucoxanthin, neoxanthin,

prasinoxanthin, violaxanthin, diadinoxanthin,

antheraxanthin, alloxanthin, diatoxanthin, lutein,

zeaxanthin, crocoxanthin, echinenone, alpha-carotene, and

beta-carotene. These standards were used to calibrate the

absorption and fluorescence detectors. In addition,

chlorophyll a and b standards were purchased at the same

time from Sigma Chemical Company. The number of

pigment calibrations, coefficient mean, standard deviation

and coefficient of variation are summarized in Table 2.

The chromatograms were analyzed and QC/QA protocols

were followed. One such QC/QA method is to plot

fluorometrically determined chlorophyhlls versus total

HPLC chlorophyll a (chlorophyllide

a, allomer and epimer chlorophyll a, monovinyl

chlorophyl a and divinyl chlorophyll a). Figure 19.1

shows this plot indicating that the fluorometric method for

estimating chlorophylls overestimates total chlorophyll a
SIMBIOS Project Annual Report

126

by some 20%. This HPLC measured pigment data was

compared to previously measured samples from a variety

of research cruises over the last 15 years. Figure 19.2

shows this plot, indicating that these pigment data bases

are not statistically different from each other.

Two intercalibration efforts on a small number of

samples were performed between two other pigment

laboratories. Duplicate samples were collected during

cruises and/or deployments and then shipped to CHORS

for analysis. This exercise showed that there was excellent

agreement when photosynthetic and photoprotectice

carotenoids were compared. Large uncertainties were

found when comparing monovinyl and divinyl chlorophyll

a concentrations for one laboratory. For the other

laboratory comparison differences were found for both

chlorophylls a and b. We have been investigating these

differences and will follow this up with additional pigment

intercalibration efforts.

19.3 FUTURE WORK

We processed all of the 1,362 pigment samples sent to

CHORS during the first year of support. Comparisons

between several laboratories on samples analyzed by the

fluorometric method were in excellent agreement (within a

few percent), where as, the HPLC inter-laboratory

comparison had larger uncertainties. There were a few

instrument malfunctions, but this not adversely effect

HPLC pigment sample processing. Laboratory

intercalibration efforts will continue so as to provide to the

project an internally consistent pigment database.

Figure 19.1: Regression model showing fluorometrically determined Chlorophyll a versus total Chlorophyll a
SIMBIOS Project Annual Report

127

determined by HPLC.

Figure 19.2: Regression model predicting accessory pigments versus total chlorophyll a, both determined by

HPLC. The graph includes SIMBIOS data, as well as data collected from the global HPLC pigment data base.
SIMBIOS Project Annual Report

128

Table 19.1: 2001 SIMBIOS-CHORS HPLC Status Log

R=received, RHPLC = run HPLC, CC/QC=concentration calculations and QC/QA,

D=delivered

2001 2001 2001 2001 2001 2001 2001 2001

PI Apr May Jun Jul Aug Sep Oct Nov

R. Stumpf R RHPLC CC/QC D 14 (33)

A. Subramaniam R RHPLC CC/QC D 149 (200)

D. Siegel R RHPLC CC/QC D 285 (299)

R RHPLC CC/QC D 107

B. Arnone R, RHPLC, CC/QC D 23

R, RHPLC, CC/QC D (45)

R (50)

R, RHPLC, CC/QC D 209

G. Mitchell R, RHPLC, CC/QC D 144

F. Chavez R, RHPLC, CC/QC D 358 (91)

R. Morrison R, RHPLC, CC/QC D 44

Y. Dandonneau R, RHPLC, CC/QC D 21

L. Harding R (18)

MOBY
SIMBIOS Project Annual Report

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TABLE 19.2: During the last six months we performed up to five different HPLC pigment calibrations for a variety of

phytoplankton pigments. The table below is a summary of these calibrations by pigment compound for the entire year (2001).

Pigment Number of Standard Coefficient of

Compound Calibrations Mean Deviation Variation

Chlorophyll a 8 5.7270E-04 2.7750E-05 5

Chlorophyll b 8 4.3070E-04 2.5130E-05 6

Chlorophyll c 3 1.1490E-04 2.5800E-05 22

Peridinin 2 3.4820E-04 6.0570E-05 17

19’Butanoyloxyfucoxanthin 4 2.5310E-04 2.0270E-05 8

Fucoxanthin 4 2.5400E-04 1.1100E-05 4

19’Hexanoyloxyfucoxanthin 4 2.5720E-04 2.7290E-05 11

Diadinoxanthin 3 1.7170E-04 1.4860E-05 9

Diatoxanthin 4 1.6800E-04 1.7340E-05 10

Lutein 3 1.8090E-04 2.8020E-05 16
SIMBIOS Project Annual Report

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Chapter 20

Assessment, Validation, and Refinement of the Atmospheric

Correction Algorithm for the Ocean Color Sensors

Menghua Wang

University of Maryland, Baltimore County, Baltimore, Maryland

20.1 INTRODUCTION

The primary focus of this proposed research is for the

atmospheric correction algorithms evaluation and

development and satellite sensor calibration and

characterization. It is well known that the atmospheric

correction, which removes more than 90% of sensor-measured

signals contributed from atmosphere in the visible, is the key

procedure in the ocean color remote sensing (Gordon and

Wang, 1994). The accuracy and effectiveness of the

atmospheric correction directly affect the remotely retrieved

ocean bio-optical products. On the other hand, for ocean color

remote sensing, in order to obtain the required accuracy in the

derived water-leaving signals from satellite measurements, an

on-orbit vicarious calibration of the whole system, i.e., sensor

and algorithms, is necessary. In addition, it is important to

address issues of (i) cross-calibration of two or more sensors

and (ii) in-orbit vicarious calibration of the sensor-atmosphere

system. The goal of these researches is to develop methods

for meaningful comparison and possible merging of data

products from multiple ocean color missions. With support

and collaboration from the SIMBIOS project office, much

efforts have been on studying and comparing the ocean color

data derived from the Japanese Ocean Color and Temperature

Scanner (OCTS) and the French Polarization and

Directionality of the Earth’s Reflectances (POLDER). OCTS

and POLDER were both on board Japan’s Sun-synchronous

Advanced Earth Observing Satellite (ADEOS) from August

1996 to June 1997, collecting about 10 months of global ocean

color data.

This provides a unique opportunity for developing

methods and strategies for the merging of ocean color data

from multiple ocean color sensors. In this report, I will very

briefly discuss other research activities, but mainly focus on

describe collective efforts (with SIMBIOS project office) in

the OCTS and POLDER comparison study.

 20.2 RESEARCH ACTIVITIES

(a) Studies have been carried out to understand various

effects on the performance of the SeaWiFS atmospheric

correction algorithm for the ocean color and atmospheric

aerosols: (i) the solar and viewing geometry effects, in

particular, for cases of the large solar and viewing zenith

angles; (ii) the Earth curvature effects, i.e., the sphericalshell atmosphere (SSA) vs. the plane-parallel atmosphere

(PPA) (Ding and Gordon, 1994); (iii) effects of the sun

glint contaminations on the derived SeaWiFS ocean and

atmospheric products (Wang and Bailey, 2001); (iv) the

effects of ocean surface wind speed on the SeaWiFS

derived aerosol optical thickness; and (v) the effects of

polarization in the aerosol lookup tables on the SeaWiFS

derived ocean color and atmosphere products. These

research works (except item (iii)) are still on going. Some

preliminary results, however, show importance and

necessary to account for some of these effects.

(b) I have been working on activities for the International

Ocean-Color Coordinating Group (IOCCG) atmospheric

correction working group. The main objective of the

working group is to quantify the performance of the

various exiting atmospheric correction algorithms used

for the various ocean color missions. Therefore, the

derived ocean color products from various ocean color

missions can be meaningfully compared and possibly

merged. As the atmospheric correction is a key procedure

in the ocean color remote sensing, we want to answer

question such as how can derived ocean color products

from one sensor be best compared with those from others.

We want to quantify the differences among the

performance of the atmospheric correction algorithms.

The core working group members are from OCTS/GLI

(Japan), SeaWiFS (US), MODIS (US), POLDER

(France), and MERIS (Europe). I am currently serving as

the working group leader.

(c) In collaboration with A. Isaacman, B. Franz, and C.

McClain and the SIMBIOS project office, we have

studied and compared the ocean color data derived the
SIMBIOS Project Annual Report

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OCTS and POLDER. The Japanese OCTS and French

POLDER were both flown on the Japanese Sunsynchronous Advanced Earth Observing Satellite

(ADEOS) from August 1996 to June 1997, collecting

about 10 months of global ocean color data. ADEOS was

on a polar orbit at an altitude of 800 km with local

crossing time (descending node) at around 10:40 AM.

This was the first time in history that two ocean color

sensors were on board the same platform and viewed the

global ocean with the same temporal and similar global

spatial coverages. Therefore, it provides an ideal case for

studying and comparing ocean color data derived from

two different sensors, hence allowing development of a

strategy for ocean color data merger from multiple ocean

color sensors. In a recent study, Wang and Franz (2000)

show that, using a vicarious inter-calibration approach

between the MOS and the SeaWiFS (SeaWiFS ocean

color data were used as “truth”), the ocean optical

property data derived from two sensors can be

meaningfully compared. The bias differences are

significantly reduced between the two products. In that

study (Wang and Franz, 2000), measurements from the

two sensors had a temporal difference of about 90

minutes. In this study, we compare the ocean color data

derived from OCTS and POLDER measurements using

consistent data processing algorithms for both sensors and

vicarious calibrations based on a common in situ data set

from the Marine Optical Bouy (MOBY) (Clark et al.,

1997) in the waters off Hawaii. Therefore, differences in

the derived ocean color products from OCTS and

POLDER are primarily associated with differences in

instrument characteristics. A paper by Wang et al. (2002)

was submitted to Applied Optics.

(d) With requests from SIMBIOS project office, I have been

helping and actively participating some on going

SIMBIOS works/activities and other activities: (i) During

Dr. Yongseung Kim’s visit from Korea in Feb. 2001, I

participated many discussions in helping for outline a

strategy of the calibration and data processing for the

Korea Ocean Scanning Multi-spectral Imager (OSMI). I

have prepared and provided the necessary tables and data

to process the OSMI data using the MSL12 and worked

on some OSMI related issues; (ii) In collaboration with

other SIMBIOS PIs and scientists, I have participated the

CZCS data reanalysis effort for the data merging activity;

(iii) I have been continuing some of the Germany

Modular Optoelectronic Scanner (MOS) related works;

and (iv) I have been actively participating the NASANASDA effort in processing the OCTS global data using

the SIMBIOS MSL12 software.

20.3 RESERCH RESULTS

Some results are briefly reported in here due to the length

limitation for the document and also due to preliminary nature

for some of on going works.

The Effects of the Wind on the SeaWiFS Aerosol Optical

Thickness

Various in situ matchup studies show that the SeaWiFS

deirved aerosol optical thickness at 865 nm is usually slightly

overestimated comparing with the in situ observations. A

study has been underway to evaluate the effects of ocean

surface wind speed on the SeaWiFS derived aerosol optical

thickness. Preliminary results show that, ignoring the surface

wind speed in generating the aerosol lookup table, the derived

aerosol optical thickness is usually overestimated by 10-20%

for the global average wind speed (7.5 m/s). This could

possibly explain why the SeaWiFS aerosol optical thickness is

slight overestimated. Therefore, the overestimation of the

SeaWiFS aerosol optical thickness is probably not from

calibration of the SeaWiFS band 8. This work is continuing.

The Earth curvature Effects on the Rayleigh Radiance

Computations

The Earth curvature effects on the performance of the

SeaWiFS atmospheric correction algorithm was studied by

Ding and Gordon (1994) for a limited sensor viewing

geometries. With some help from H. R. Gordon, some

sensitivity studies have been conducted for a more complete

solar and viewing geometries for the Earth’s curvature effects

on the Rayleigh radiance computations for the SeaWiFS

wavelengths. Figure 20.1 provides example of the ratio values

of the Rayleigh radiance computed using the spherical-shellatmosphere (SSA) and the plane-parallel-atmosphere (PPA)

for the various solar and viewing geometries and for the

SeaWiFS 8 spectral bands. Note that the current SeaWiFS

Rayleigh tables were computed using the PPA model. Figs.

1(a)-1(d) are ratio of the Rayleigh radiance values (SSA/PPA)

for the solar zenith angles from 0°-80°, and for sensor zenith

angles of 40°, 60°, 70°, and 80°, respectively. The relative

azimuthal angle of 90° was used in the simulations. As

expected, for the small solar and/or sensor zenith angles, the

Earth curvature effects are negligible (ratios are nearly to 1),

while for the very large angles there are some large radiance

differences for the radiance computed using the PPA and SSA

models. This work is still on going.
SIMBIOS Project Annual Report

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The Solar and Viewing Geometry Effects

Simulations have been carried out for understanding of

the solar and viewing geometry effects, in particular, for cases

of the large solar and viewing zenith angles, on the

performance of the SeaWiFS atmospheric corrections. Results

show that, for maritime aerosols, the SeaWiFS atmospheric

correction algorithm performed well for the solar and sensor

zenith angles ranging from 0°-80°. However, there are some

geometry dependences of results on the performance of the

SeaWiFS atmospheric correction algorithm, more so on the

relative azimuthal angle than the solar and sensor zenith

angles. This work is continuing.

Selected Results from OCTS/POLDER Comparisons

A series of measurements from OCTS and POLDER over

the Sargasso Sea were collected for comparison. The satellite

observations were obtained from a co-located 3°×3° box over

the Sargasso Sea (latitude of 23.5°N to 26.5°N and longitude

of 70°W to 73°W). Over the Sargasso Sea, OCTS data were

all collected through the ground station at Wallops, Virginia.

Therefore, OCTS data were processed from Level-0 to Level1B using the SIMBIOS code, and then to Level-2 using

MSL12. Figure 20.2 compares the histogram (%) of the

retrieved normalized water-leaving radiance, [ ( )] Lw N λ ,

between OCTS and POLDER over the Sargasso Sea. Over the

Sargasso Sea, OCTS and POLDER data were acquired on

November 20 and December 19 of 1996, and January 26,

February 17, March 11, and April 14 of 1997 (there were data

from each month for six months). Figs. 20.2(a)-2(f) are

comparison results for the Sargasso Sea for data acquired from

November 20, 1996 to April 14, 1997, respectively. The

OCTS and POLDER [ ( )] Lw N λ at 490 and 565 nm usually

compare quite well, with small variations between the two

measurements, while the POLDER [ ( )] Lw N λ at 443 nm shows

significant variations compared with the OCTS results

(sometimes high and sometimes low). The shape of the

histogram distributions from OCTS and POLDER, however,

are generally similar. It was found that with our approach in

processing OCTS and POLDER data, there are no obvious

bias differences in the derived [ ( )] Lw N λ between the two

sensors, whereas the noise differences are difficult to correct

since they depend mainly on the sensor characteristics.

20.4 FUTURE WORK

Research works will be continued to evaluate the various

effects as discussed in this report on the performance of the

atmospheric correction and aerosol retrieval algorithms. It is

planed to study and understand aerosol model effects and

possibly to include other aerosol models, e.g., large particle

size sea-salt aerosols (Porter and Clarke, 1997) and dust

aerosol models (Longtin et al., 1988) for the dust study. It will

also be continued in studying and development of the

vicarious calibration and inter-calibration techniques from the

in situ and/or various ocean color sensors measurements. This

work will be certainly helpful to development of data merging

techniques from multiple ocean color missions.

REFERENCES

Clark, D.K., H.R. Gordon, K.J. Voss, Y. Ge, W. Broenkow,

and C. Trees 1997: Validation of atmospheric correction

over the ocean, J. Geophys. Res., 102: 17,209-17,217.

Ding, K., and H.R. Gordon 1994: Atmospheric correction of

ocean-color sensors: effects of the Earth's curvature, Appl.

Opt., 33: 7096-7106.

Gordon, H.R., and M. Wang 1994: Retrieval of water-leaving

radiance and aerosol optical thickness over the oceans

with SeaWiFS: A preliminary algorithm, Appl. Opt., 33:

443-452.

Longtin, D.R., E.P. Shettle, J.R. Hummel, and J.D. Pryce

1988: A Wind Dependent Desert Aerosol Model:

Radiative Properties, AFGL-TR-88-0112, , U.S. Air

Force Geophysics Laboratory, Hanscom Air Force Base,

Mass.

Porter, J.N., and A.D. Clarke 1997: Aerosol size distribution

models based on in situ measurements, J. of Geophys.

Res., 102: 6035-6045.

Wang, M., and S. Bailey 2001:, Correction of the sun glint

contamination on the SeaWiFS ocean and atmosphere

products, Appl. Opt., 40: 4790-4798.

Wang, M., and B.A. Franz 2000: Comparing the ocean color

measurements between MOS and SeaWiFS: A vicarious

intercalibration approach for MOS, IEEE Trans. Geosci.

Remote Sens., 38: 184-197.

Wang, M., A. Isaacman, B.A. Franz, and C.R. McClain 2002:

Ocean color optical property data derived from OCTS and

POLDER: A comparison study, Appl. Opt.(Accepted).
SIMBIOS Project Annual Report

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Figure 20.1: The ratio values of the Rayleigh radiance computed using the spherical-shell-atmosphere (SSA) and the planeparallel-atmosphere (PPA) for the SeaWiFS 8 spectral bands and for the solar zenith angles from 0°-80° and sensor zenith

angles of (a) 40°, (b) 60°, (c) 70°, and (d) 80°. The relative azimuthal angle of 90° was used.
SIMBIOS Project Annual Report

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0

5

10

15

20

25

0 0.5 1 1.5 2 2.5 3

 OCTS

 POLDER

% of Retrievals

[Lw(λ)]N (mW cm-2 μm-1 sr-1)

Sargasso Sea (Nov. 20, 1996)

443 nm

490 nm

565 nm

(a)

0

5

10

15

0 0.5 1 1.5 2 2.5 3

% of Retrievals

[Lw(λ)]N (mW cm-2 μm-1 sr-1)

Sargasso Sea (Dec. 19, 1996)

490 nm 443 nm

565 nm

(b)

0

5

10

15

0 0.5 1 1.5 2 2.5 3

% of Retrievals

[Lw(λ)]N (mW cm-2 μm-1 sr-1)

Sargasso Sea (Jan. 26, 1997)

490 nm 443 nm

565 nm

(c)

0

5

10

15

20

0 0.5 1 1.5 2 2.5 3

% of Retrievals

[Lw(λ)]N (mW cm-2 μm-1 sr-1)

Sargasso Sea (Feb. 17, 1997)

443 nm

490 nm

565 nm

(d)

0

4

8

12

16

0 0.5 1 1.5 2 2.5 3

% of Retrievals

[Lw(λ)]N (mW cm-2 μm-1 sr-1)

Sargasso Sea (Mar. 11, 1997)

443 nm

490 nm

565 nm

(e)

0

6

12

18

0 0.5 1 1.5 2 2.5 3

% of Retrievals

[Lw(λ)]N (mW cm-2 μm-1 sr-1)

Sargasso Sea (Apr. 14, 1997)

443 nm 490 nm

565 nm

(f)

Figure 20.2: The histogram (%) of the OCTS-retrieved normalized water-leaving radiances at wavelengths 443, 490, and 565

nm compared with those from POLDER measurements over the Sargasso Sea for (a)-(f) from Nov. 20, 1996 to Apr. 14, 1997.
SIMBIOS Project Annual Report

135

This Research was supported by

 the NASA SIMBIOS Grant # NAS5-00203

PEER REVIEWED PUBLICATIONS

Wang, M., B.A. Franz, R.A. Barnes, and C.R. McClain, 2001:

Effects of spectral bandpass on SeaWiFS-retrieved nearsurface optical properties of the ocean, Appl. Opt., 39, 343-

348.

Wang, M. and S. W. Bailey, 2001: Correction of the Sun Glint

Contamination on the SeaWiFS Ocean and Atmosphere

Products, Appl. Opt., 40, 4790-4798.

Accepted

Chou, M. D., P. K. Chan, and M. Wang, Aerosol radiative

forcing derived from SeaWiFS-retrieved aerosol optical

properties, J. Atmos. Sci. (In press).

Eplee, R. E., Jr., W. D. Robinson, S. W. Bailey, D. K. Clark,

P. J. Werdell, M. Wang, R. A. Barnes, and C.R. McClain

The Calibration of SeaWiFS, Part 2: Vicarious Techniques,

Appl. Opt. (In press).

Wang, M., A. Isaacman, B.A. Franz, and C.R. McClain Ocean

Color Optical Property Data Derived from OCTS and

POLDER: A Comparison Study, Appl. Opt. (Accepted).

Wang, M., The Rayleigh Lookup Tables for the SeaWiFS

Data Processing: Accounting for the Effects of Ocean

Surface Roughness, Int. J. Remote Sens. (Accepted).

Submitted

Gregg, W. W., M. E. Conkright, J. E. O’Reilly, F. S. Patt, M.

Wang, J. Yoder, and N. Casey-McCabe, The NOAANASA CZCS reanalysis effort, Appl. Opt. (Submitted).

PRESENTATIONS

Wang, M. and S. W. Bailey, 2000: Correction of the sun glint

contamination, presented at the 5th Pacific Ocean Remote

Sensing Conference, Goa, India.

Wang, M., 2001: The IOCCG atmospheric correction working

group status report, presented at The Sixth IOCCG

Committee Meeting, La Jolla, California.

Wang, M., 2001: Retrieval of the aerosol optical properties

from the SeaWiFS measurements, presented at The

AeroCenter Seminar Series, NASA/GSFC, Greenbelt,

Maryland.

Wang, M., 2001: Retrieval of ocean near-surface optical and

microphysical properties from spaceborn sensors:

Principles, Techniques, and Applications, Invited series of

lectures presented at the National Satellite Ocean

Application Service, Beijing, China.

Wang, M., 2001: Ocean color remote sensing, presented at the

National Satellite Meteorology Research Institute, Beijing,

China.

Wang, M., 2001: The current status of the atmospheric

correction algorithms and vicarious calibration techniques

for the ocean color remote sensing, Invited seminar

presented at the Second Institute of Oceanography,

Hangzhou, China.
136

Chapter 21

Measurements of the Vertical Structure of Aerosols and Clouds

Over the Ocean Using Micro-pulse LIDAR Systems

Ellsworth J. Welton

Goddard Earth Sciences and Technology Center, University of Maryland Baltimore County, Greenbelt,

Maryland

James D. Spinhirne

NASA Goddard Space Flight Center, Greenbelt, Maryland

James R. Campbell

Science Systems and Applications, Inc., Lanham, Maryland

Timothy A. Berkoff

Goddard Earth Sciences and Technology Center, University of Maryland Baltimore County, Greenbelt,

Maryland

David Bates

Physics Department, University of Miami, Coral Gables, Florida

21.1 INTRODUCTION

The determination of the vertical distribution of aerosols

and clouds over the ocean is needed for accurate retrievals of

ocean color from satellites observations. The presence of

absorbing aerosol layers, especially at altitudes above the

boundary layer, has been shown to influence the calculation of

ocean color [Gordon et al., 1997a]. Also, satellite data must be

correctly screened for the presence of clouds, particularly

cirrus, in order to measure ocean color [Gordon et al, 1997b].

One instrument capable of providing this information is a

lidar, which uses pulses of laser light to profile the vertical

distribution of aerosol and cloud layers in the atmosphere.

However, lidar systems prior to the 1990s were large,

expensive, and not eye-safe which made them unsuitable for

cruise deployments. During the 1990s the first small,

autonomous, and eye-safe lidar system became available: the

micro-pulse lidar, or MPL [Spinhirne et al., 1995]. The MPL

is a compact and eye-safe lidar system capable of determining

the range of aerosols and clouds by firing a short pulse of laser

light (523 nm) and measuring the time-of-flight from pulse

transmission to reception of a returned signal. The returned

signal is a function of time, converted into range using the

speed of light, and is proportional to the amount of light

backscattered by atmospheric molecules (Rayleigh scattering),

aerosols, and clouds. The MPL achieves ANSI eye-safe

standards by sending laser pulses at low energy (μJ) and

expanding the beam to 20.32 cm in diameter. A fast pulserepetition-frequency (2500 Hz) is used to achieve a good

signal-to-noise, despite the low output energy. The MPL has a

small field-of-view ( ̃100 μrad) and signals received with the

instrument do not contain multiple scattering effects. The

MPL has been used successfully at a number of long-term

sites and also in several field experiments around the world

[Welton et al., 2000; Peppler et al., 2000; Voss et al., 2001;

Welton et al., 2001a].

In 1999, members of the Micro-pulse Lidar group at

GSFC submitted a proposal to organize a worldwide network

of MPL systems to provide long-term measurements of the

vertical distribution of clouds and aerosols at sites around the

world. The original proposal also included support for a

limited number of field experiments each year. In the summer

of 2000, NASA EOS agreed to fund the proposal, and the

MPL-Net project [Welton et al., 2001b] was started. The

CERES validation group at NASA LaRC contributed four

MPL systems to MPL-Net, supplementing the existing stock

of instruments. Finally, in the fall of 2000 the NASA

SIMBIOS program agreed to fund another proposal to conduct

lidar measurements from research vessels at sea using the

SIMBIOS MPL system. This effort provides the SIMBIOS
137

project with continuous observations of aerosol and cloud

vertical distributions during up to two cruises per year.

21.2 RESEARCH ACTIVITIES

ACE-Asia Cruise

The third Aerosol Characterization Experiment (ACE)

was conducted in Asia during March and April 2001. The

experiment, referred to as ACE-Asia, utilized inland and

island ground stations, aircraft, and ship platforms to study

dust and pollution aerosols over Asia and the surrounding

region. The Asian derived aerosols are important to satellite

based ocean color measurements because the dust and

pollution plumes transport far over the North Pacific Ocean.

Dust absorbs sunlight in the visible wavelengths, and its

vertical distribution over the ocean can affect the retrieval of

ocean color.

The SIMBIOS MPL was deployed onboard the NOAA

ship R/V Ronald H. Brown during ACE-Asia in order to

determine the height of Asian aerosol plumes over the ocean.

The MPL was operated from March 22 to April 20, 2001.

Measurements were not continuous because of temperature

stability problems in MPL van. The A/C unit on the van was

damaged during a storm early in the cruise, and reliable

temperature control was not possible for the remainder of the

cruise. The MPL was operated only when the temperatures

could be monitored manually, resulting in approximately 30%

to 40% downtime over the whole cruise. The MPL ran

continuously otherwise.

The MPL signals are stored at 1-minute time intervals,

with a range resolution of 0.075 km from sea level up to a

maximum altitude of 20 km. The raw data are converted into

uncalibrated lidar signals, referred to as Normalized Relative

Backscatter (NRB), using procedures discussed in Campbell et

al. (2001) and Welton and Campbell (2001). The NRB signals

are then analyzed to produce profiles of aerosol extinction and

optical depth, and the layer averaged extinction-to-backscatter

ratio, using techniques discussed in the Appendix of Welton et

al. (2001a).

MPL Instrument Development

The original MPL design used a fixed-place detector that

was part of the optical path within the instrument. If the

detector failed, it could not be replaced without performing a

complete re-alignment of the MPL. This is not possible at sea.

As a result, the health of the detector determined whether or

not a particular cruise was a failure or a success. In order to

overcome this problem, the MPL system was modified to

incorporate a fiber-coupled detector. The detector itself is the

same, however, it is no longer part of the optical path. It is

connected to the optical path by a fiber cable. The detector can

now be replaced by simply unscrewing the old detector from

the fiber, and replacing it with a new one. This can be

accomplished at sea with a minimum of effort, and can be

performed by anyone with a small amount of training.

21.3 RESEARCH RESULTS

Figure 21.1 shows a plot of the NRB signals vs. day of

year during the ACE-Asia cruise. The gaps in data, due to the

problems discussed in the previous section, are shown in

black. Regions of higher signal indicate the presence of

aerosols and clouds. The period around day 100, April 10, is

of interest because the ship was in the Sea of Japan and

positioned downwind of a massive dust storm that occurred a

few days earlier over China. Preliminary aerosol optical data

products have been generated for this time period. Figure 21.2

shows profiles of the NRB signal, extinction, and optical depth

on April 10 at 0500 UTC. The aerosols are confined to two

distinct layers. The lower layer extends from 0 to 2 km, and

the upper from about 3.5 to 5.5 km. Cirrus is also present from

10 to 14 km, but is not included in the extinction retrieval

because it is a cloud.

The aerosol extinction-to-backscatter ratio, S, for the

lower and upper layers was 32 and 66 sr, respectively. S is

proportional to the aerosol phase function at 180o and

inversely proportional to the aerosol single scattering albedo.

Layers with a high value of S tend to have a lower single

scattering albedo, and therefore more aerosol absorption

relative to layers with a lower value of S. The preliminary

results shown here indicate that the upper aerosol layer was

more absorbing than the lower layer. Therefore, the aerosols

observed on this day may have influenced the retrieval of

ocean color in this region.

This observation also serves as an example of the MPL’s

ability to distinguish aerosol layers from clouds. The

microtops sunphotometer on the ship measured optical depths

over 1 at 500 nm, at least 0.8 larger than the aerosol optical

depth from the MPL. However, the NRB signal shows that a

cirrus cloud was present during this time. The cirrus cloud

optical depth was approximately 0.8 based on the difference

between the microtops and MPL results.

21.4 FUTURE WORK

The results from the ACE-Asia cruise are still being

analyzed. The NRB data has been fully processed and files are

available on the SEABASS archive and also on the MPL-Net

web-site (http://virl.gsfc.nasa.gov/mpl-net/). The remaining

data products will be available only on the MPL-Net web-site.

Some of these data are available now for the purpose of

preparing papers, however the data are not quality assured.

Final, quality assured extinction and optical depth data from

ACE-Asia will be available by spring 2002 at the latest.

The results from the ACE-Asia cruise will be used

together with other MPL-Net measurements made during the

experiment to assess whether or not aerosol transport models

predict the correct vertical distribution. This work will become
138

the focus of a paper over the course of the next several

months. In addition, numerous publications, as co-authors, are

planned with other researchers involved in ACE-Asia studies.

The MPL will be readied for the next series of

deployments during 2002. A cruise is planned in the North

Atlantic Ocean during the spring. This cruise will be aimed at

studying the vertical distribution of Saharan dust over the

ocean. Finally, the MPL will also be made available for a

cruise scheduled in the summer. Our group will not participate

directly in the cruise, but will help support the MPL

measurements and analysis.

REFERENCES

Campbell, J.R., D.L. Hlavka, E.J. Welton, C.J. Flynn, D.D.

Turner, J.D. Spinhirne, V.S. Scott, and I.H. Hwang. 2001:

Full-time, Eye-Safe Cloud and Aerosol Lidar Observation

at Atmospheric Radiation Measurement Program Sites:

Instrument and Data Processing, J. Atmos. Oceanic

Technol., in press.

Gordon, H. R., T. Du, and T. Zhang. 1997a: Remote sensing

of ocean color and aerosol properties: resolving the issue

of aerosol absorption, Appl. Optics, 36, 8670-8684.

Gordon, H. R., T. Zhang, F. He, and K. Ding. 1997b: Effects

of stratospheric aerosols and thin cirrus clouds on

atmospheric correction of ocean color imagery:

Simulations, Appl. Optics, 36, 682-697.

Peppler, R.A., C.P. Bahrmann, J.C. Barnard, J.R. Campbell,

M.D. Cheng, R.A. Ferrare, R.N. Halthore, L.A. Heilman,

D.L. Hlavka, N.S. Laulainen, C.J. Lin, J.A. Ogren, M.R.

Poellot, L.A. Remer, K. Sassen, J.D. Spinhirne, M. E.

Splitt, and D.D. Turner. 2000: ARM Southern Great

Plains Site Observations of the Smoke Pall Associated

with the 1998 Central American Fires, Bull. Amer.

Meteor. Soc., 81, 2563-2591.

Spinhirne, J. D., Rall, J., and V. S. Scott. 1995: Compact eyesafe lidar systems. Rev. Laser Eng., 23, 26-32.

Voss, K.J., E.J. Welton, P.K. Quinn, J. Johnson, A.

Thompson, and H. Gordon. 2001: Lidar Measurements

During Aerosols99, J. Geophys. Res., 106, 20821-20832.

Welton, E.J., K.J. Voss, H.R. Gordon, H. Maring, A. Smirnov,

B. Holben, B. Schmid, J.M. Livingston, P.B. Russell, P.A.

Durkee, P. Formenti, M.O. Andreae. 2000: Ground-based

Lidar Measurements of Aerosols During ACE-2:

Instrument Description, Results, and Comparisons with

other Ground-based and Airborne Measurements, Tellus,

B52, 635-650.

Welton, E.J., K.J. Voss, P.K. Quinn, P.J. Flatau, K.

Markowicz, J.R. Campbell, J.D. Spinhirne, H.R. Gordon,

and J.E. Johnson. 2001a: Measurements of aerosol

vertical profiles and optical properties during INDOEX

1999 using micro-pulse lidars, J. Geophys. Res., in press.

Welton, E.J., J.R. Campbell, T.A. Berkoff, J.D. Spinhirne, S.

Tsay, and B. Holben. 2001b: First Annual Report: The

Micro-pulse Lidar Worldwide Observational Network,

Project Report, http://virl.gsfc.nasa.gov/mpl-net/.

Welton, E.J., and J.R. Campbell. 2001: Micro-pulse Lidar

Signals: Uncertainty Analysis, J. Atmos. Oceanic

Technol., submitted.

82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110

12

11

10

9

8

7

6

5

4

3

2

1

12

11

10

9

8

7

6

5

4

3

2

1

Day Of Year (UTC)

Altitude (km)

0.000 0.025 0.050 0.075 0.100

Normalized Relative Backscatter PhE/(usec*shot)

Figure 21.1: The Normalized relative backscatter (NRB) signals at 1-minute time resolution are plotted versus the day

of the year during the ACE-Aia cruise.
139

0500 UTC.

0 0.05 0.1

NRB Signal (Ph/(usec*shot))

0

2

4

6

8

10

12

14

16

0 0.05 0.1 0.15

Aerosol Extinction (km)-1

0 0.05 0.1 0.15 0.2

Aerosol Optical Depth

Cirrus Cloud

Figure 21.2: Profiles of the NRB signal, aerosol extinction, and aerosol optical depth are shown for 04/10/2001 at 0500 UTC
140

Chapter 22

Measurements and modeling of apparent optical properties of

ocean waters in support to ocean color data calibration,

validation, and merging

David Antoine, André Morel, Marcel Babin and Hervé Claustre

Laboratoire d’Océanographie de Villefranche, CNRS-INSU, Villefranche sur Mer, France

Stanford B. Hooker

 NASA/GSFC, Greenbelt, Maryland

22.1 INTRODUCTION

This progress report does not show any results in terms of

in situ data because the buoy’ deployment is only planned for

late January 2002. The advancement of the buoy development

and testing, of the instrument integration and testing, and of all

other preparatory activities (in particular monthly cruises to

the deployment site) are succinctly described here. The

objectives and the analyses that are planned thanks to the data

to be collected are also reminded. This project is supported by

the European Space Agency (ESA/ESTEC contract N°

14393/00/NL/DC), the French Space Agency “Centre National

d’Etudes Spatiales (CNES)”, the “Centre National de la

Recherche Scientifique (CNRS-INSU)” and the “Observatoire

Océanologique de Villefranche sur mer”. A prerequisite to

building long-term (over decades) archives of ocean color, in

response to the need for assessing the response of the oceanic

biota to climate changes, is to accurately calibrate the top-ofatmosphere satellite observations, then to validate the surface

geophysical parameters derived from these observations.

Ensuring coherence between these geophysical products, as

derived from different sensors, is also an important aspect to

consider. When ocean color observations from different

sensors are considered in view of data merging, their crosscalibration and validation might be facilitated if it could be

“anchored” on continuous long-term in situ stations (IOCCG,

1999). Deploying and maintaining moorings that operate in a

continuous way is, however, a difficult task.

In response to these concerns, we propose to carry out

match-up analyses and vicarious calibration experiments,

based on a data set to be built from a permanent marine optical

buoy. This new type of marine optical buoy has been

specifically designed for the acquisition of radiometric

quantities, and has been already deployed in the

Mediterranean sea in July 2000 (a deployment of 3 months for

validating the mooring concept), between France and Corsica.

The vicarious calibration experiments should allow the

top-of-atmosphere total radiance to be simulated and

compared to the satellite measurements, in particular for the

European MERIS sensor. By this way, the need for a change

of the pre-flight calibration coefficients for a given sensor

might be evaluated, and its amount quantified. From this data

set, match-up analyses shall be also possible for chlorophyll

concentration and water-leaving radiances, as well as

algorithm evaluation (atmospheric correction and pigment

retrieval). Because of a certain commonality in the band sets

of the new-generation ocean color sensors, the data acquired

with the buoy might be used for several of these sensors, and

then contribute to the international effort of cross-calibrating

them and of cross-validating their products, which are

amongst the basic goals of SIMBIOS. In addition, some

protocol issues (measurements) are specifically linked to the

use of buoys, while others, of general concern to marine optics

measurements, may find specific answers in the case buoys

are used. We propose to examine these aspects, which, to our

knowledge, have not been thoroughly investigated up to now.

Description of the planned analyses

Two main “vicarious” calibration paths exist to produce

ocean color products of the desired accuracy, i.e., waterleaving radiances within an error of about 5% in the blue for

an oligotrophic ocean (Gordon, 1997, Antoine and Morel,

1999). The first one is usually referred to as “vicarious

calibration”, and consists in forcing the satellite-derived

water-leaving radiances to agree with a set of in situ waterleaving radiances (“match-up analyses”). A set of “vicarious

calibration coefficients” is therefore obtained, which is applied

to the Top-of-atmosphere (TOA) total radiances measured by

the sensor. The second procedure, which is also an indirect

(“vicarious”) calibration is sometimes referred to as a

“radiometric calibration”, and consists in simulating the TOA

signal that the sensor should measure under certain conditions,
141

and to compare it to the measured signal. Inconveniences of

the first type of “vicarious calibration” is that it is dependent

upon the procedure used for the atmospheric correction of the

TOA observations. The advantage of this technique is,

however, and besides the fact that atmospheric measurements

are not needed, that the marine signals delivered by several

sensors that use different atmospheric correction algorithms

can be cross-calibrated provided that the same set of in situ

water-leaving radiances is used to perform the vicarious

calibration. This is presently the case, for instance, for the

SeaWiFS and OCTS sensors. Inconveniences of the vicarious

“radiometric calibration” is that it requires a set of in situ

measurements that is usually difficult to gather, among other

things because a high accuracy is needed. In addition to the inwater measurements of the water-leaving radiances, this data

set includes : sea state and atmospheric pressure, ozone

concentration, aerosol optical thickness, aerosol type, and

even aerosol vertical profile if the aerosols reveal to be

absorbing. If this data set is successfully assembled, the great

advantage of the vicarious “radiometric calibration” is that it is

independent of the atmospheric correction algorithms, so that

the TOA signals of various sensors can be cross-calibrated.

Then it is up to any user to apply its preferred atmospheric

correction to these TOA signals. The marine signals in that

case might be inconsistent if significant differences exist in the

various atmospheric corrections.

Match-up analyses

Match-up analyses should be possible for water-leaving

radiances, as well as for chlorophyll concentrations, which are,

however, only collected on a monthly basis (HPLC), during

servicing to the mooring. The chlorophyll fluorescence is

recorded in a continuous way, yet we cannot ascertain that its

conversion into chlorophyll units, based on the monthly HPLC

determinations, will be able to generate concentrations with an

accuracy suitable for match-ups.

Vicarious calibration experiments

This is probably one of the most challenging parts of this

project, aside from technological development. Indeed, we

should have in hand all the necessary tools and data to

generate TOA total radiances and to compare them to their

“satellite equivalent” (e.g., see Gordon, 1998), yet it would be

risky to warrant in advance that a 5% absolute accuracy (i.e.,

what is aimed at with the new-generation ocean color sensors,

e.g., Gordon , 1997) will be reached.

The greatest difficulty, at least to our opinion, lies in a

correct estimation of the aerosol optical thickness, phase

function, and single scattering albedo. These parameters are

accessible through the inversion of sun photometer

measurements, yet uncertainties inevitably occur when

applying such methods. Assembling all data needed for these

vicarious calibration experiments will be often compromised

only because aerosol parameters are not accurate enough.

Nevertheless, we hope that over the 3 years of the project,

enough relevant data will be gathered for that purpose.

Therefore, we will set up a “toolbox” based on codes available

at LOV, and which will be fed with all observation listed in

the preceding sections, with the aim of generating the TOA

radiances needed for vicarious calibrations. The error budget

of these computations will be also examined. This may

include, not exhaustively, the assessment of the impact of (1)

the various ways of computing the water-leaving radiance

from the below-water measurements of radiances and

irradiances, (2) the uncertainties in the representation of

surface roughness, and (3) the selection of a given RT code.

Inversion of the sun photometer measurements in order to

get the aerosol optical thickness will follow the usual

procedure, as described for instance in Frouin et al. (2000).

There exist a variety of algorithms for the inversion of the sky

radiance measurements (Wang and Gordon, 1993; Nakajima

et al., 1983, 1996; Dubovik and King, 2000; Dubovik et al.,

2000). Evaluation of these various algorithms is out of our

scope, yet we will try to implement the most adapted ones in

order to get some idea about the uncertainties attached to our

retrievals. The routine processing performed by the

AERONET will anyway already provide the necessary data,

so that implementation of procedures at LOV will mostly be

motivated by scientific purposes, which not necessarily

intervene in the frame of the MERIS cal/val activities.

When the desired data are obtained, they will be used as

inputs and boundary conditions to radiative transfer

calculations. We can use several codes available at LOV,

namely a Monte Carlo code (cross-validated with respect to

other codes in Mobley et al., 1995), the Hydrolight® code

(Mobley, 1994), and the MOM code (Matrix Operator

Method, developed by J. Fischer and collaborators at the Free

University of Berlin). The simulated TOA total radiances will

be then compared to their equivalent extracted from MERIS

full resolution images. The principle is the following (L is for

radiance, and the subscripts “t” for total TOA, “g” for sun

glint, “wc” for white caps, and “w” for water-leaving; T and t

and the direct and diffuse atmospheric transmittances,

respectively):

 Atmospheric pressure Radiative transfer

 Sea state (wind speed) Calculations

 Aerosol optical thickness

 Aerosol type (π0, β(θ)) 

 Ozone concentration

 Geometry Measured simulated Lt = Lpath + T.Lg + t.Lwc + t.Lw

 Lt

Selection of the relevant geometry

and sea state so that T.Lg = t.Lwc = 0

In water radiometers
SIMBIOS Project Annual Report

142

Preparatory activities: Buoy’s design

The design of the buoy, managed by the ACRI-in

company, started in fall 1999 and led to a totally new type of

optical buoy. The following paragraphs summarize the

rationale for its design and the main characteristics of the

system. Platforms developed for oceanographic purposes are

rarely adapted to the deployment of radiometers at sea. Indeed,

recording the light field within the ocean interior is difficult

because the instruments themselves and, more dramatically,

the platform onto which they are installed, inevitably

introduce perturbations. Other difficulties originate from the

need to keep the instruments as much horizontal as possible,

either because a plane irradiance is aimed at (cosine sensor),

or if a given direction (generally nadir) is aimed at. The actual

measurement depth is also difficult to accurately assess,

because rapid vertical displacements of the instruments

sometimes occur, which prevent any precise estimation of

pressure, thence of depth. Considering the above observations

(among others), we have developed a new type of platform,

dedicated to radiometry measurements, able to minimize

shadowing effects, to minimize perturbation of the sub-marine

light field, and to warrant the stability of the instruments.

The principle is that of a “reversed pendulum”, with

Archimedes thrust replacing gravity. A large sphere (Ø 1.8 m)

is stabilized at a 18 meters depth out of the effect of most

swells (a cable goes down to the sea floor), and creates the

main buoyancy. Above the sphere, a rigid, tubular, structure is

fixed, which hosts the instrumentation onto horizontal arms (at

5 and 9 meters). An ~3 tons thrust ensures the stability of the

system, which is subject to very limited forces from the socalled “transparent-to-swell” superstructure. With such a

design, there is no large body at the surface generating shade.

Reduced-scale model of the buoy and tests in an engineering

pool

A reduced scale model (scale 1/10) has been developed in

order to verify the theoretical predictions, and it has been

tested in an engineering pool (dimensions in meters 24x16x3,

with a 10-meters shaft), by applying to it several types of

swells and currents (mono chromatic or random swells, up to 5

meters real scale). Results of these tests have fully confirmed

the theoretical predictions that were previously carried out, in

terms of horizontal and vertical displacements (the latter are

extremely low), as well as in terms of angular deviations from

the vertical. For instance, the mean tilt of the buoy is ~4° (with

±~4° of pitching), for a 4.6-meter swell of period 5.2 seconds.

These tests have also confirmed that no “hidden defect”,

hardly discernible via calculations only, was compromising

the feasibility of the system. The tests were fully conclusive

and led to the construction of the first “beta version” of the

buoy, in aluminum.

Qualification by a 3-month test deployment of the buoy on site

The full scale “beta version” was built in spring 2000

(Figure 22.1). The material was aluminum. It is made of two

parts, the lower one which is from –20 meters to –9 meters

below the surface and consists of the sphere and a simple

tubular structure, and the upper one which goes from –9

meters below the surface to +4.5 meters above the surface, and

which hosts the instrumentation. The full-scale beta version

has been deployed on site during 3 months (20 of July to 20 of

October, 2000), which allowed to encounter a variety of

meteorological situations. The buoy was equipped with two

inclinometers, a pressure sensor, an ARGOS beacon and a

flashlight. The goal of this deployment was to qualify the new

concept of buoy as well as to identify possible problems

necessitating modifications. The results of the qualification

deployment showed a certain lack of righting torque, with

consequences on the behavior of the buoy when the swell

becomes greater than about 2 meters. Although not dramatic,

this behavior is not totally satisfactory, so that it was decided

to introduce slight modifications in the design and

construction of the buoy in order to improve the percentage of

time for which the requirements in terms of inclination is

satisfied.

Modifications introduced in the buoy’s design and

construction

One modification has been introduced in the design, by

installing the sphere at 17 meters instead of 18 meters. By this

way, the distance from the center of the sphere to the fixation

of the buoy to the mooring cable is one meter greater (the

righting torque is therefore much larger). Another

modification has been introduced in the materials used to build

the upper part of the buoy (the one hosting the

instrumentation), from aluminum to carbon composite. A

specific study has been conducted to check that this composite

material was adapted to our problem, which is definitely the

case.

The impacts of these 2 changes on the upper part of the

system are : rigidity is improved, weight is diminished by the

2/3, corrosion is eliminated, the drag coefficient is reduced by

the use of tubes with a lower diameter, and the elastic limit is

larger than it was in comparison to the forces imposed by

breaking waves. At the end, a net gain of 60% in the righting

torque has been obtained for the full buoy. The first

deployment of this modified version of the buoy, fully

equipped, is planned for no later than the end of January 2002,

weather permitting.
143

The instrumentation on the buoy

The full system, developed by Satlantic Inc., has been

delivered in Villefranche on the 24th of October, 2001. Its

physical integration on the buoy and the subsequent testing

will probably last until mid December 2001. It is reminded

that the system comprises:

• Radiometers of the Satlantic 200 series, measuring Es (at

+4.5 meters above surface), and Ed, Eu, and Lu (nadir) at

2 depths, namely 5 and 9 meters. The 2-axis tilt and the

pressure are also recorded. The buoy orientation with

respect to the sun is also recorded.

• Fluorometers, at the 2 same depths, for a proxy to Chl.

• Transmissometer, at the deepest sampling depth, i.e., 9

meters, for a proxy to the particle load.

• CTD, at 9 meters, mainly for pressure and temperature.

• Backscattering meter for a proxy to bb at 2 wavelengths

(443 and 560 nm), at 9 meters.

• The set of parameters directly derived from the

measurements is described in section 11.

• From these measurements, various AOPs or IOPs might

be derived, as the water-leaving radiance, Lw, the diffuse

attenuation coefficients for upwelling and downwelling

irradiance, Ku and Kd, the attenuation coefficient for

upwelling radiance, KL, the diffuse reflectance just below

the sea surface, R, the “nadir Q” factor, Eu/Lu, the

attenuation and backscattering coefficients, c and bb. The

absorption coefficient, a, will be tentatively derived

through inversion of the AOPs (using for instance Kd and

R).

All measurements will be simultaneously collected, and

centralized by a unique (acquisition / storage /

communication) system, that will merge the data and send

them to the visiting ship via a RF link. Part of the data is

transmitted in real time by an ARGOS link (only for checking

that everything’s working well and that batteries are Ok).

The coastal site for atmospheric measurements

A coastal site (Cap Ferrat, in front of the laboratory)

should be equipped with an automatic sun photometer station,

introduced within the AERONET. This equipment should

provide continuous record of the sky radiances at this site,

from which information about aerosol types and aerosol

optical thickness will be retrieved.

Atmospheric measurements : ship-of-opportunity

In order to increase the number of determination of the

aerosol optical thickness on the buoy site, a portable sun

photometer shall be installed on the ships that daily cross from

Nice to Corsica, and which go near the buoy site. The

operation of such an instrument is simple enough to expect a

significant percentage of good-quality measurement, if

performed by trained crewmembers. These operations are

under negotiation with the company that manages the ships.

Set up of the monthly additional measurements and

BOUSSOLE site characterization

Eighteen days of ship time have been obtained in 2001

from CNRS-INSU, as 6 campaigns of 3 days. They were

initially planned for starting the monthly servicing of the

buoy, in case it would have been deployed in July 2001 (initial

plan). Because the buoy is still not deployed, the first 4 of

these campaigns (end of July, beginning of September,

October and November) have been used to prepare and

organize most of the activities that will be necessary during

monthly servicing to the buoy. These activities have included:

• Verification of the mooring line by divers (still on site;

only the buoy has been removed).

• Deployment of several optical profilers (50 vertical

profiles have been acquired), including the LOV’ SPMR,

the NASA’ MicroNESS (collaboration Stan Hooker) and

a new hyper-spectral profiler developed by Satlantic Inc.

(collaboration M. Lewis).

• Pigment determination (HPLC; 35 surface samples).

• CTD profiles (25 profiles).

• Inter-comparison between above-water and below-water

determination of the water-leaving radiances.

• Characterization of the spatial heterogeneity of the site,

by following a grid pattern with a fluorometer, the pattern

being then calibrated in terms of chlorophyll

concentration by taking discrete samples along the grid.

The remaining two campaigns to be carried out until the

end of 2001 (beginning and mid of December) will be devoted

to the same kind of activities, and to the testing of the

instrumentation of the buoy (using a rosette frame to be

deployed from the ship). The other operations and

measurements that will be progressively introduced in 2002

are :

• Replacement of radiometers by divers

• Cleaning/verification of the buoy
144

• IOP profiles (absorption, attenuation), IOP determination

on the HPLC samples.

Inter-comparison between above-water and in-water

determination of the water-leaving radiance

As part of the preparatory activities that we carry out in

the frame of BOUSSOLE, we have pursued the comparative

evaluation of the above-water determination of the waterleaving radiances against the under-water determination of this

key parameter. The above-water data have been collected with

a SIMBADA radiometer (developed and maintained by LOA

in Lille, France; data processing also performed by LOA), and

the in-water determination from our SPMR profiler and the

MicroNESS profiler (NASA; only in July). The data are still

under processing and the results of these inter-comparisons

will be probably available in 2002. From these results, it will

be decided whether or not it is useful to continue the abovewater measurements.

Plans for calibration of the radiometers

The SQM-II (i.e., the high stability lamp used for relative

calibration) will be kept in the laboratory, and we will replace

each month one set of instruments by a new, cleaned and

calibrated, set of instruments. The recovered set of

radiometers is brought back to the lab for calibration, cleaning,

and repair if necessary. One month later, the exchange is again

performed and so on. We first have set up the installation of

the SQM-II and have worked towards establishing the routine

operations that will be necessary each month on each set of

radiometers (the so-called “SQM sessions”).

Bidirectionality of the ocean reflectance: Selection of

adequate IOP for Case 1 waters

A revision of the inherent optical properties of oceanic

waters, which includes the consideration of the absorption

values by pure waters (Pope and Fry, 1997), has been

published (Morel and Maritorena, 2001). In addition, a new

parameterization of the particle volume scattering function

(VSF) has been developed, which allows the shape of the VSF

to be regularly changing with the chlorophyll concentration

(Chl). With these VSF, the backscattering ratio is decreasing,

from 1.23 to 0.45 % when Chl increases from 0.01 to 10 mg

m-3. These values are more realistic when introduced in a

reflectance modeling, and when the modeled results are

compared to actual reflectance spectra. They remove the

weaknesses which previously resulted from the use of a single

particle VSF (that of Petzold, with a backscattering ratio of

1.9%), whatever Chl. The shape and magnitude of the VSF

are crucial in driving the angular pattern of the upward

radiance field, and thus the BRDF properties (and Tables).

The recent radiative transfer computations and the production

of the BRDF Lookup Tables have been performed by using

these revisited IOPs.

Raman scattering influence on the upward radiance field and

bidirectional properties.

In addition, the computations of these tables have been

carried by accounting for the inelastic Raman scattering. The

BRDF parameters are, as expected, sensitive to this emission

for Chl values up to 0.3, even 1 mg m-3 (in the red part of the

spectrum). These Tables have been transferred to NASA.

They are commented in detail in a Chapter of the revised

version of the Ocean Optics Protocols for Satellite Ocean

Color Sensor Validation (Version 3) prepared by J. Mueller

(Chapter XIII « Normalized water-leaving radiance and

remote sensing reflectance : Bidirectional reflectance and

other factors »). This chapter describes the implication of

bidirectionality onto ocean optics measurements, particularly

in the frame of calibration of satellite data through in situ

measurements (above and in-water determinations of waterleaving radiances). The bidirectional properties have also

been summarized and their consequences examined when the

merging of various satellite data is envisaged (Correction of

bidirectional effects for an optimal binning of ocean color

data). This will be a part of a document currently prepared by

a Ad Hoc group, set up by IOCCG in January 2001 (D.

Antoine, Chairman), with the aim of examining the binning

issues of ocean color data.

REFERENCES

Antoine, D. and A. Morel, 1999: A multiple scattering

algorithm for atmospheric correction of remotely-sensed

ocean colour (MERIS instrument) : principle and

implementation for atmospheres carrying various aerosols

including absorbing ones, Int. J. Remote Sensing 20, 1875-

1916.

Dubovik, O.and M.D.King, 2000: A flexible inversion

algorithm for retrieval of aerosol optical properties from

Sun and sky radiance measurements, J. Geophys. Res. 105,

20673-20696.

Dubovik, O., A.Smirnov, B.N.Holben, M.D.King, Y.J.

Kaufman, T.F.Eck, and I.Slutsker, 2000: Accuracy

assessments of aerosol optical properties retrieved from

AERONET Sun and sky-radiance measurements, J.

Geophys. Res. 105, 9791-9806.
145

Frouin R., B. Holben, M. Miller, C. Pietras, E. Ainsworth, J.

Porter, and K. Voss, 2000: Sun and Sky radiance

measurements and data analysis protocols, in “Ocean

optics protocols for satellite ocean color sensor validation,

revision 2”, NASA/TM-2000-209966, NASA GSFC, 108-

124.

Gordon, H. R., 1997: Atmospheric correction of ocean color

imagery in the Earth observing system era, J. Geophys.

Res. 102, 17081-17106.

Gordon, H.R., 1998, In-Orbit calibration strategy for ocean

color sensors, Rem. Sens. Environ. 63, 265-278.

IOCCG, 1999: Status and plans for satellite ocean-colour

missions : considerations for complementary missions.

Yoder, J.A. (ed.), Reports of the International OceanColour Coordinating Group, No. 2, IOCCG, Dartmouth,

Canada.

Mobley, C.D., 1994: Light and water : radiative transfer in

natural waters, Academic Press, London,92 pp.

Mobley, C. D., Gentili, B., Gordon, H. R., Jin, Z., Kattawar,

G. W., Morel, A., Reinersman, P., Stamnes, K., and Stavn,

R. H., 1993: Comparison of numerical models for

computing underwater light fields, Appl. Opt. 32, 7484-

7504.

Morel, A. and S. Maritorena, 2001: Bio-optical properties of

oceanic waters : a reappraisal J. Geophys. Res. 106, 7163-

7180.

Nakajima, T., M. Tanaka, and T. Yamauchi, 1983: Retrieval

of the optical properties of aerosols from aureole and

extinction data. Appl. Opt. 22, 2951-2959.

Nakajima, T., G. Tonna, R. Rao, P. Boi, Y. Kaufman, and B.

Holben, 1996: Use of sky brightness measurements from

ground for remote sensing of particulate polydispersions.

Appl. Opt. 35, 2672-2686.

Pope R. M. and E. S. Fry. 1997: Absorption spectrum (380-

700 nm) of pure water. II. integrating cavity

measurements. Appl. Opt. 36: 8710-8723.

Wang, M. and H.R. Gordon, 1993: Retrieval of the columnar

aerosol phase function and single scattering albedo from

sky radiance over the ocean : simulations, Appl. Opt. 32,

4598-4609.
146

Fig. 22.1: The “BOUSSOLE“ buoy at sea (“beta version” deployed on site during 3 months in 2000 without instrumentation,

for testing the principle) Once at sea, the top of the structure is at + 4.5 metres above the surface. The immersed part goes down

to 20 metres, where it is linked to a Kevlar cable going down to the seafloor (2440 m). The cap of the buoy (here a fictitious

one, just for tests) will host the radiometer for measurement of downwelling irradiance, solar panels, an ARGOS beacon and

other equipment for transmission of data to the visiting ship.
147

Chapter 23

Contribution To A Global Climatology Of Surface Pigments From

Ocean Color Sensors

Pierre-Yves Deschamps , Hubert Loisel, Jean Marc-Nicolas, Guislain Becu and François Thieuleux

LOA, Université de Lille 1, France

Yves Dandonneau

LODYC, Université de Paris 6, France

Cécile Dupouy

 IRD, Nouméa, Nouvelle Calédonie

Robert Frouin

Scripps Institution of Oceanography, University of California San Diego, California

Cyril Moulin

LSCE, CEA, Gif-sur-Yvette, France

Jacques Neveux

CNRS, Banyuls-sur-Mer, France

23.1 INTRODUCTION

A group of French scientists concerned with the

utilization of ocean color proposes to coordinate its efforts

with the SIMBIOS program. The main objectives of this

proposal are:

• to contribute to in-situ data collection effort centralized in

SeaBASS, to be used for the CAL/VAL of different ocean

color space sensors, including methodological

improvement such as spectrofluorometry,

• to investigate the effect of biological variability on

geophysical products (pigment concentration, marine

reflectances) derived from ocean color,

• to assess the accuracy of the present processing of

POLDER data vs SeaWiFS and others, by comparing

Level-3 products, and by applying POLDER algorithms

to SeaWiFS data,

• to improve the atmospheric correction in presence of

absorbing aerosols.

GeP&CO Cruises (LODYC, Banyuls, IRD, LOA)

The GeP&CO cruises aim to describe the variability

(quantitative, qualitative) of phytoplankton populations, and to

estimate its impact on the ocean's biogeochemistry. The

cruises use a merchant ship that sails quarterly from Le Havre

(France) to Nouméa (New Caledonia), see Figure 23.2. The

sampled areas include a large variety of oceanic conditions

(temperate to equatorial, oligotrophic to eutrophic) which are

observed using the same analytical instruments and methods,

giving to GeP&CO a global character. The following

measurements are made:

• Chlorophyll (mono and divinyl) and related

pheopigments, carotenoids, and phycoerythrin, using

HPLC and spectrofluorimetry
148

• Picoplankton (Prochlorococcus, Synechococcus,

picoeucaryotes and bacteria) using flow cytometry

• Coccolithophorids, by microscope

• Light absorption spectra by the phytoplankton and by

non-algal particles

• Light absorption spectra by dissolved organic matter

• Seawater reflectance using SIMBADA radiometer

• Nutrients (nitrate+nitrite, silicate, phosphate), TCO2 and

alcalinity, salinity and SST

To date, eight cruises have been made from October 1999

to August 2001. GeP&CO will stop in August 2002, i. e. a

total of 12 cruises. Pigment data allowed to characterize the

different populations in the phytoplankton communities (for

instance: divinyl-chlorophyll a for Prochlorococcus sp.,

phycoerythrin for Synechococcus sp. or other cyanobacteria

and cryptophyceae, 19'hexanoyloxyfucoxanthin for

coccolithophorids and Phaeocystis sp., fucoxanthin for

diatoms), and to use pigments ratios as proxies for the f-ratio

(new production to net primary production). Validity of this

approach can be tested using the identification and counts of

cells by flow cytometry and microscopy. In addition, the

GeP&CO data will also be used to validate algorithms that are

being developed for the retrieval of geophysical parameters at

sea. 2D (excitation, emission wavelength) fluorescence

spectra are routinely measured in order to compute the

concentration of chlorophylls (see above). We have also used

these 2D spectra to "hindcast" the chlorophyll a and

pheophytin a concentrations that would be obtained using the

ancient fluorescence-acidification technique. Comparison of

the results shows that the actual assemblages of pigments are

dominated by chlorophyll a and divinyl-chlorophyll a

everywhere, so that hindcasted ancient chlorophyll a values

are tightly related to the sum chlorophyll a and divinylchlorophyll a (r2

=.999) and overestimate this sum by 5% (in

preparation). It also shows that ancient pheophytin a results

are not reliable. The results from this study may help to make

a link between field chlorophyll data obtained before and after

the introduction of modern techniques in pigments

measurements.

We have also defined a protocol for the measurement of

2D fluorescence spectra on phytoplankton populations

collected at the surface of membrane filters, in order to

estimate the water soluble pigments, essentially

phycoerythrins. The main difficulty arises when main

fluorescence excitation and emission peaks are very close and

the diffusion of excitation spectra are mixed. This difficulty

seems to be solved by properly filtering out the blank in this

method (in preparation). A cross checking of pigments

determinations by HPLC in the GeP&CO program has been

made recently by duplicating some measurements in

collaboration with Charles Trees at San Diego State

University Foundation. Results from this comparison will be

available by the end of November. For further information, see

http://www.lodyc.jussieu.fr/gepco

SIMBAD/SIMBADA network (LOA, SIO)

The SIMBAD/SIMBADA optical radiometers have been

specifically designed for to measure in situ the necessary

parameters for the validation of ocean color sensors. They

measure the aerosol optical thickness and the water leaving

radiance/above-water reflectance, by aiming from the

shipdeck successively to the Sun, then to the water surface at

about the Brewster angle (a polarizer strongly reduces the

reflected sky radiation). 10 SIMBAD radiometers with 5

spectral bands from 443 to 870 nm have been realized by

LOA; 5 of them have been operated since 1996 by LOA, 3 by

SIO, and 2 by the SIMBIOS project. A series of 25

SIMBADA radiometers with 11 wavelengths from 350 to 870

nm are under realization by LOA and SIO; their deployment

has begun at the end of 2000 and it is expected that about 15

of them will be in operations by the end of 2001, and the

others one by mid 2002. Further information on the

SIMBAD/SIMBADA networks is available on:

http://www-loa.univ-lille1.fr/simbadA/

http://polaris.ucsd.edu/~simbad/

Figure 23.1 gives the statistics of the measurements by the

LOA network from 1996. To avoid duplicates, clear sky

measurements have been binned every 15 minutes. Fig. 23.1a

gives the histogram of the monthly number of aerosol optical

thickness measurements, Fig. 23.1a, and of the marine

reflectance, Fig. 23.1b. Note that SIMBADA is replacing

SIMBAD progressively from February 2001.

Next, SIMBAD measurements will be delivered to the

SeaBASS data base. SIMBADA measurements will be

continued in the future thanks to the support of CNES and the

MERIS/ESA project.

Validation of SeaWiFS geophysical products (LOA, LODYC,

SIO)

Figure 23.2 gives the location of SIMBAD/SeaWiFS

matchups, during GeP&CO cruises in the North Atlantic and

South Pacific, and during ACE-ASIA in the North Pacific.

Validation of chlorophyll and marine reflectances during

GeP&CO Cruises

Figure 23.3a gives a comparison between the marine

reflectances from SIMBAD and chlorophyll (TChla = Chla +
149

DivChla, measured by spectrofluorometry) measured during

the 4 first GeP&CO cruises, 1999-2000, and the SeaWiFS

geophysical products, Level 3b, daily data binned at 9 km

spatial resolution. The matchups cover the North Atlantic and

the South Pacific, with a range of in-situ values of the

chlorophyll concentration measurements from 0.03 to 5

mg/m3. A matchup is declared when an in situ measurement

has been done during the daylight coincident with the

SeaWiFS acquisition. When comparing surface chlorophyll

concentration, Fig. 23.3a, SeaWiFS slightly overestimate the

chlorophyll by 11 %, and the rms of the difference is 37 %.

The comparison between reflectance measurements at 443,

490, and 565 nm, Fig. 23.3b, gives similar results, biases less

than 12 % and rms less than 20 %. These results are excellent

and demonstrate the good quality of in-situ measurements and

SeaWiFS estimates.

The final data set, 12 cruises,1999-2001, will be further

analyzed in an attempt to understand what are the weight of

different error sources that may explain the differences shown

in the comparison: in-situ or satellite? SeaWiFS radiometry or

data processing? atmospheric correction or biooptical

algorithm?

Validation of aerosol products during ACE-ASIA

During GeP&CO most of the measured aerosol optical

thickness are very low, and the comparison with SeaWiFS

products is not significant. But a very attractive data set has

been acquired during ACE-ASIA 2001and the results are

shown in Fig. 23.4. They are matchups between SIMBADA

measurements and SeaWiFS products, LAC when available,

or Level 3b. The aerosol optical thickness, Fig. 23.4a, is

accurately retrieved by SeaWiFS, but SeaWiFS underestimate

strongly its wavelength dependence represented by its

Angstrom coefficient, Fig. 23.4b. The reason of the

discrepancy on the Angstrom coefficient seems to be linked

to SeaWiFS algorithm. The consequence of an underestimate

of the aerosol wavelength dependence by SeaWiFS should be

an underestimate of the aerosol scattering, thus an

overestimate of the marine reflectances at ocean color

wavelengths. This is not what is shown in Fig. 23.5 showing a

comparison of the spectra of marine reflectance during ACEASIA, measured in-situ by SIMBADA, and retrieved from

SeaWiFS, for the 6 days. SeaWiFS derived reflectances are

lower than the in situ ones in the blue region, and the effect is

increasing toward the shorter wavelength. This is typical of

the error committed by the SeaWiFS atmospheric correction

algorithm in the presence of absorbing aerosols, mostly

pollution aerosols in the ACE-ASIA case.

POLDER (LOA, LSCE)

POLDER-2 is now scheduled for launch in November

2002. For this perspective, we are developing an advanced

data processing that will be used to process POLDER-2 and

reprocess POLDER-1. The main improvements are:

• absorbing aerosol detection and improved atmospheric

correction in their presence,

• improved atmospheric correction over turbid waters,

• inversion of marine reflectances to derive water

absorption and scattering coefficients as products.

SeaWiFS data as well as POLDER-1 data are currently used to

check the new algorithm.

Figure 23.6 gives an example of the detection of desert

absorbing aerosol outflowing from the North Sahara, using

POLDER data. More information on POLDER is available on

http://www-projet.cst.cnes.fr:8060/POLDER/index.html

and global images of chlorophyll, November 1996 to June

1997 are available on line on:

http://www-projet.cst.cnes.fr:8060/POLDER/SCIEPROD/oc9611.htm

POLDER-2 Level-3 products will include an additional

product somewhat similar to the SeaWiFS L3 mapped

product. Daily, 10-day and monthly global maps of the main

marine parameters, projected on a regular 1/12° latitudelongitude grid, will be available in HDF-EOS format to ensure

full compatibility with other ocean color data set. The final

definition of this product will benefit of the recommendations

of the IOCCG Working Group on Data Binning Issues.

Atmospheric correction (LOA, LSCE)

LSCE is working on alternative algorithms to improve

atmospheric correction in the presence of absorbing aerosols, a

situation that generally leads to the failure of current

algorithms. An extensive work has been performed in

collaboration with Howard Gordon (University of Miami) to

adapt the Spectral Matching algorithm (Gordon et al., 1997) to

the Atlantic dust-zone off the coast of Africa. Results show

that significant improvements can be obtained in atmospheric

correction by using such advanced algorithms. This algorithm

has recently been implemented within SeaDAS and will be

used to improve ocean color retrievals in the Eastern

Mediterranean in the framework of a collaboration with

researchers of the Middle East Technical University (Turkey).

This basin is of particularly challenging in terms of

atmospheric correction since it regularly experiences very
150

different aerosol influences, from desert dust to fire and

pollution plumes within a few days.

LSCE is also exploring the capabilities of advanced

numerical methods to speed up this kind of improved

atmospheric correction algorithms. Neural network

assemblages are currently being tested on the Spectral

Optimization algorithm of Chomko and Gordon (1998). This

work is performed within the framework of the NAOC

(Neural Network for Advanced Ocean Color) project funded

by the European Community, and we expect soon to be able to

report on the results.

Pierre-Yves Deschamps and Jean-Marc Nicolas (LOA)

participate to the comparison of atmospheric algorithms,

sponsored by IOCCG, funded by SIMBIOS and organized by

Menghua Wang, reported elsewhere in this report. They are

working on the adaptation of the POLDER atmospheric

correction to the SeaWiFS processing, and will report soon on

the results.

Cyril Moulin (LSCE) participates to the Working Group

on Data Binning Issues, sponsored by IOCCG and organized

by David Antoine. These two activities are of particular

importance since we are still actively working on the

definition of both algorithm and products for POLDER-2.

This research is supported by the following French

organisations: CEA, CNES, CNRS, IRD.

PUBLICATIONS

Dupouy C., H. Loisel, J. Neveux, C. Moulin, S. Brown, J.

Blanchot, and A. Lebouteiller. Light-absorption and

backscattering coefficients by microbial groups along

180° during an ENSO cold phase in the Pacific Ocean: a

simulation test and an inversion of a POLDER image. J.

Geophysical Res., submitted.

Frouin, R., S. F. Iacobellis, and P. Y. Deschamps, 2001:

Influence of oceanic whitecaps on the global radiation

budget. Geophy. Res. Lett., 28, 1523-1526.

Loisel, L., E. Bosc, D. Stramski, K. Oubelker, and P-Y.

Deschamps, 2001: Seasonal variability of the

backscattering coefficients in the Mediterranean Sea

based on Satellite SeaWIFS imagery. Geophysical Res.

Lett., 22, 4203-4206.

Mackay, D. J., J. Blanchot, H. W. Higgins, and J. Neveux,

Phytoplankton community structure in the Equatorial

Pacific. Deep-Sea Res., in press.

Moulin, C., H. R. Gordon, V. F. Banzon, R. H. Evans, 2001:

Assessment of Saharan dust absorption in the visible from

SeaWiFS imagery, J. Geophys. Res., 106, 18239-18250.

Moulin, C., H. R. Gordon, R. M. Chomko, V. F. Banzon, R.

H. Evans, 2001: Atmospheric correction of ocean color

imagery through thick layers of Saharan dust, Geophys.

Res. Lett., 28, 5-8.

Nicolas, J. M., P. Y. Deschamps, and R. Frouin, Spectral

reflectance of oceanic whitecaps in the visible and near

infrared: Aircraft measurements over open ocean,

Geophys. Res. Lett., in press.

Radenac, M.-H., C. Menkes, J. Vialard, C. Moulin, Y.

Dandonneau, T. Delcroix, C. Dupouy, A. Stoens, and P.

Y. Deschamps 2001: Modeled and observed impacts of

the 1997-1998 El Niño on nitrate and new production in

the equatorial Pacific. J. Geophys. Res., 106 , C11 ,

26879-26898.
151

Figure 23.1: Histograms of the monthly number of SIMBAD(A) measurements of a) aerosol optical thickness, b) marine

reflectances.
152

Figure 23.2: Location of SeaWiFS/SIMBAD(A) matchups during GeP&CO 1999-2000 (North Atlantic and South Pacific)

and ACE-ASIA 2001 (North Pacific)
153

Figure 23.3a: SeaWiFS chlorophyll estimates (Level 3b) vs in-situ chlorophyll (TChla = Chla + DivChla) surface

measurements by spectrofluorometry.
154

Figure 23.3.b: SeaWiFS marine above water reflectances (Level 3b, the water leaving radiances have been normalized by the

solar downward surface irradiance) vs in-situ SIMBADA measurements during GeP&CO 1999-2000.
155

Figure 23.4a: SeaWiFS estimate vs in-situ SIMBADA measurements of the aerosol optical thickness at 870 nm.
156

Figure 23.4b: SeaWiFS estimate vs in-situ SIMBADA measurements of the Angstrom coefficient.
157

Figure 23.5: Spectral reflectances derived from SeaWiFS (LAC, Level 2) and measured in-situ by SIMBADA for some ACEASIA 2001stations. The SeaWiFS underestimation increases when wavelength decreases, a typical effect of absorbing

aerosols.
158

Figure 23.6: POLDER detection of absorbing aerosols over a decade, March 10-19 1997. The algorithm makes use of

POLDER multiangle viewing. By this time, the two main sources of absorbing aerosols are located in India and the Sahara

desert.
159

Chapter 24

CNES Contribution to Ocean Color Calibration : Multi-sensor

Cross-Calibration over Desert Sites

Bertrand Fougnie, François Cabot, Olivier Hagolle, and Patrice Henry

Centre National d'Etudes Spatiales, Toulouse, France

24.1 INTRODUCTION

Our proposal to the SIMBIOS project was a threefold

investigation on calibration of ocean color sensors : i/ a multiocean color sensor cross calibration thanks to our rich

database over desert sites spectrally and bidirectionnaly

characterized; ii/ a calibration of MERIS and MODIS over

Rayleigh scattering which is a method successfully applied for

various sensors like POLDER, Végétation or SPOT sensors;

and iii/ a vicarious calibration of POLDER-2 using in-situ

measurements. This report is focused on the multi-sensor

cross-calibration over desert sites.

24.2 RESEARCH ACTIVITIES

Desert sites are terrestrial sites with remarkable

properties. After a selection based on statistical description, 20

desert sites were carefully selected for

• large extent but with a good spatial homogeneity to

minimize misregistration and various field-of-view impact

• temporal stability of the surface including low variation of

the overlaying atmosphere and poor annual mean cloud

coverage

• small bidirectional effects

These sites are located in Africa and Arabia and are

100*100 km2 and some in-situ measurements were conducted

over some of them (see Cosnefroy et al., 1996). Such

terrestrial sites can be used as references for on-orbit sensors

for different aspects : absolute calibration (which often

requires additional in-situ measurements, see for instance

Henry et al. 2000 for Végétation), large field-of-view multiangular calibration (Fougnie et al.; 2000 for Végétation or

Hagolle et al., 1999 for POLDER/ADEOS). Another

important application is the cross-calibration of two or more

sensors. This method has the advantage to do not refer to the

absolute calibration but only give a relative comparison of the

calibration of the considered sensors.

The first step of this exercise was to build a consistent

database with acquisitions of several sensors over the 20

selected desert sites. This was done in our team and these sites

are now fully characterized in term of spectral and

bidirectional signature (Cabot et al., 1999). This database

includes data from POLDER, Végétation, AVHRR, ATSR,

SeaWiFS and now MISR and MODIS data. The earth

observing sensors planned to be launched soon will be

integrated in this database (MERIS, AATSR, Végétation-2,

POLDER-2, GLI, MODIS-PM).

The data processing includes both spectral and

bidirectional considerations. For this, the following processing

is done : an atmospheric correction is made on the top-ofatmosphere radiances of the sensor-1 used as reference in

order to estimate ground surface reflectance in the sensor-1

spectral bands. A spectral fit is applied afterwards to the

surface reflectance of sensor-1 (see Cabot et al., 1998) and,

considering the spectral response of sensor-2, the surface

reflectance corresponding to the spectral band of sensor-2 are

derived. This spectral fit is very accurate if the spectral bands

of sensor-1 and 2 are nearly identical. The atmospheric

contribution is finally added to compute the Top-ofAtmosphere radiances for sensor 2. It is important to take into

account the fact that the sensor-1 aims the surface for a given

viewing geometry that is not generally the same that the

sensor-2's one. We usually consider POLDER as the reference

sensor because of its enlarged field-of-view capabilities that

allow a quasi-complete angular sampling of the BRDF of the

site (in addition, reciprocity principle is used to extent

geometric coverage). In this case we use two different

approach : i/ a polynomial fit is performed on the BRDF and

the reflectance corresponding to the viewing geometry of

sensor-2 is calculated using this fit or ii/ we simply choose the

closest POLDER geometry to compare to the sensor-2

geometry. These two methods provide nearly identical results.

As a result POLDER can be used as a transfer radiometer

allowing to cross-calibrate for instance two sensors with

different viewing geometry. POLDER has provided

acquisitions over desert sites from October 1996 to June 1997.
160

24.3 RESEARCH RESULTS

The first step was to update the POLDER-1 data in our

base consequently to the last reprocessing of mid-2000 which

includes a reappraisal of the POLDER-1 in-flight calibration

after some algorithmic improvement (see Fougnie et al.

2001b) and a correction of a non linearity defect of the

radiometric sensitivity (see Fougnie et al. 2001a).

The cross-calibration between POLDER and SeaWiFS

previously published in Cabot et al. (1999) was therefore

reevaluated and extended to a larger period (now nearly 4

years) as presented in Figures 24.1 and Table 24.1. The first

important result is that multi-temporal evolutions of all the

spectral bands are detected. The evolutions found for the large

wavelengths, i.e. for 670, 765, and 865nm with decreases of

respectively 1.6, 5.6 , and 6.1%, corroborate those shown in

Barnes et al. (2001) using lunar acquisitions and reminded in

Table 24.1. On the other hand, decreases of typically 1 to 2%

per year are detected for shorter wavelengths in the blue and

green part of the spectrum. This is an important result because

Barnes et al. (2001) suppose that bands 3 and 4 (490, and

510nm) are stable in time to normalize the lunar time

correction made on the SeaWiFS level-1 data processing.

The second main result is an evaluation of the in-flight

radiometric calibration coefficients. The coefficients estimated

by this method and presented in Table 24.1 are referenced to

the SeaWiFS in-flight official coefficients used to generate

level-1 data : a value of 1.0 means that there is consistency

with the official calibration coefficients. For bands 555, 670,

765 and 865 the results are consistent within 2% with those of

Barnes et al. (2001). On the other hand, large discrepancies

are found for 443, 490, and 510nm with variations of 5.6; 5.2,

and 4.3%. Table 24.1, a consistent correction is given by

Eplee et al. (2000a) using a vicarious adjustment at 490nm of

4.1%, and partly for 443 and 510nm with an adjustment of

about 1%. It has to be considered that gains measured over

desert sites are established for relatively high radiances and

are representative to the radiometric sensitivity above bilinear

knees described in Barnes et al. (2001) and in-flight calibrated

in Eplee et al. (2000b). Consequently, it is necessary to

transfer the results of Table 24.1 from above to under the

bilinear knee for a further interpretation.

Finally, future investigations using Rayleigh scattering

will be conducted and should be useful to complement these

both aspects (calibration coefficients and multi-temporal

evolutions).

The first cross-calibration of MODIS, MISR and

SeaWiFS previously published in Cabot et al. (2001) was

updated in Table 24.3. In order to be aware of some saturation

over bright desert sites, the MODIS data considered were 412

and 443nm with a ground resolution of 1km (bands 8 and 9),

469 and 555nm with a ground resolution of 500meters (bands

3 and 4), 645 and 858nm with a ground resolution of

250meters (bands 1 and 2). For MISR, 7 cameras were

considered and the first important point is that a strong

dispersion of the behavior of each camera was clearly

evidenced (Cabot et al.; 2001). Thus, variations up to 10% of

the calibration coefficients were obtained over desert sites as

shown in Table 24.2 where variations of each camera are

given in reference to the nadir viewing camera, An. This

variation of the cross-calibration with the camera should be

considered in the future, particularly for ocean color

applications for which calibration is a critical aspect. The

calibration coefficients of the nadir An camera are given in

Table 24.3 for the 4 spectral bands and a consistency within

3.5% is found with SeaWiFS. The multi-temporal evolution of

the An camera is important and is found to be about 1% to 2%

per month depending of the spectral band : this result is

established for a period of 5 months in mid-2000. For MODIS,

the accordance is within 3-4% with the exception of the

865nm spectral band for which a discrepancy of 7% is found.

Our MODIS level-1archive being limited to a short period of

time, it was not possible to determine a multi-temporal

variation of the radiometric sensitivity.

23.4 CONCLUSION

A cross-calibration of the POLDER-1, SeaWiFS, MODIS

and MISR ocean color sensors has been performed. The main

results are a possible decrease in time of the SeaWiFS bands 3

and 4 of typically 1 to 1.5% per year, a strong variation of the

behavior of the MISR camera, a relatively good coherence

within 3% between MISR and SeaWiFS, and finally an

accordance within 3-4% between MODIS and SeaWiFS,

except marginally for the 858nm band. These results are

important informations available and helpful for discussion in

the merging activities, complementary to vicarious calibration

approach for instance using in-situ measurements. Other ocean

color sensors will be soon available to complement this set of

cross-calibration, such as MERIS and POLDER-2.

REFERENCES

Barnes, R. A., R. E. Eplee, G. M. Schmidt, F. S. Patt, and C.

R. McClain, 2001: The Calibration of SeaWiFS, Part 1:

Direct Techniques, Appl. Opt., in press..

Cabot, F., O. Hagolle, H. Cosnefroy, and X. Briottet, 1998:

Inter-calibration using desert sites as a reference target,

Proc of IGARSS'98, Seattle. 

Cabot, F., O. Hagolle, C. Ruffel, and P. Henry, 1999: Use of

remote sensing data repository for in-flight calibration of
161

optical sensors over terrestrial targets, Proceedings of

SPIE'99, Denver.

Cabot, F., O. Hagolle, and P. Henry, 2001: Calibration of

MODIS and MISR over desertic sites, Investigator

Working Group, Fort Lauderdale, Florida, January 30th -

February 1st.

Cosnefroy, H., M. Leroy, X. Briottet, 1996: Selection and

characterization of Saharan and Arabian desert sites for

the calibration of optical satellite sensors, Remote Sensing

of Environ., Vol. 58, N°1, pp 101-114.

Eplee, R. E., and C. R. McClain, 2000a: MOBY data analysis

for vicarious calibration of SeaWiFS bands 1-6, SeaWiFS

Postlaunch Calibration and Validation Analyses, Part 1.,

NASA Tech. Memo. 2000-206892, Hooker and Firestone,

Eds., NASA GSFC, vol. 9, 43-50.

Eplee, R. E., and F.C. Patt, 2000b: Cloud-top radiance

analysis for SeaWiFS bilinear gain knee calibration,

SeaWiFS Postlaunch Calibration and Validation

Analyses, Part 1., NASA Tech. Memo. 2000-206892,

Hooker and Firestone, Eds., NASA GSFC, vol. 9, 13-16.

Fougnie, B., P. Henry, F. Cabot, A. Meygret, and MarieChristine Laubies, 2000: Végétation: In-flight Multiangular Calibration, 45th SPIE annual Meeting, San

Diego, California.

Fougnie, B., O. Hagolle, and F. Cabot, 2001a: In-flight

measurement and correction of non-linearity of the

POLDER-1'S sensitivity, 8th Symposium of the

International Society for Photogrammetry and Remote

Sensing, Aussois, France.

Fougnie, B., O. Hagolle, and F. Cabot, 2001b: POLDER Inflight Calibration : at http://wwwprojet.cst.cnes.fr:8060/POLDER/index.html.

Hagolle, O., P. Goloub, P.-Y. Deschamps, H. Cosnefroy, X.

Briottet, T. Bailleul, J.M.Nicolas, F. Parol, B. Lafrance,

and M. Herman,1999: Results of POLDER In-Flight

Calibration," IEEE Transactions on Geoscience and

Remote Sensing, vol. 37, 1550-1566.

Henry, P., F. Cabot, B. Fougnie, O. Hagolle, F. Meunier, and

A. Meygret, 2000: VEGETATION Absolute Calibration,

Végétation 2000, Belgirate, Italy.
162

Table 24.1: Synthesis of results for SeaWiFS. [α] is the vicarious gain from Eplee et al. (2000a). [dAo/dt] is established for

the period from 14/02/97 to 29/05/01, i.e. typically 1530 days. [dA] from Barnes et al. (2001). Results at 412nm are not

reported.

Band Ao dAo/dt

(%/year)

dAo

(860 days)

α dA

(860 days)

443 0.946 -1.58 -3.77 0.991 -0.5

490 0.950 -2.16 -5.16 0.960 0.0

510 0.958 -1.49 -3.55 0.986 0.0

555 0.977 -1.19 -2.84 0.994 -0.3

670 0.993 -0.71 -1.69 0.95 -0.8

765 0.977 -2.37 -5.66 0.946 -3.4

865 1.000 -2.59 -6.18 1.000 -9.9

Table 24.2: Variation of the calibration coefficient for fore and aft cameras compared to the nadir viewing camera (An) and for

the 4 MISR spectral bands. The last line gives the monthly evolution in percent for the An camera (established for 5 months in

mid-2000).

Camera Blue Green Red NIR

Cf 2.00 4.67 -0.56 -1.04

Bf 11.74 1.74 3.75 -1.49

Af 10.33 5.10 4.81 -0.05

An 0.00 0.00 0.00 0.00

Aa 9.05 6.50 3.45 -1.62

Ba 11.59 11.36 6.29 3.64

Ca 6.16 8.62 -0.68 1.24

%/month

An -2.21 -1.35 -0.74 -0.85

Table 24.3. Cross-calibration between SeaWiFS and POLDER-1, MODIS, and MISR (nadir camera). (1) result for 412 are

considered as not accurate. (2

) result is not definitive because still in reprocessing.

POLDER / SeaWiFS

412 443 490 510 555 670 765 865

(1) 0.946 0.950 0.958 0.977 0.993 0.977 1.000

MODIS/SeaWiFS

412 443 469 555 645 858

0.982 0.957 0.987(2) 0.969 1.034(2) 1.069

MISR(nadir)/SeaWiFS

BLUE GREEN RED NIR

0.999 0.964 0.977 1.010
163

412 nm

Ao=1.049

-0.51%/yr

rmse=0.051

443 nm

Ao=0.946

-1.58%/yr

rmse=0.055

490 nm

Ao=0.950

-2.1%/yr

rmse=0.060

510 nm

Ao=0.958

-1.4%/yr

rmse=0.055

555 nm

Ao=0.977

-1.1%/yr

rmse=0.047

670 nm

Ao=0.993

-0.7%/yr

rmse=0.040

765 nm

Ao=0.977

-2.3%/yr

rmse=0.046

865 nm

Ao=1.000

-2.5%/yr

rmse=0.048

Figures 24.1.: Time series of the calibration coefficient estimated over desert sites for the 8 SeaWiFS spectral bands.

The resulting Ao are given in reference to the official calibration parameters at the begin of the life. The mean annual

variation in %/year (= slope of the dashed lines) and the root-mean square error are reported too.
164

Chapter 25

Summary of the OSMI Radiometric Calibration Efforts

Yongseung Kim, Donghan Lee and Sungu Lee

Satellite Operation and Application Center, Korea Aerospace Research Institute, Korea

25.1 INTRODUCTION

The ocean scanning multispectral imager (OSMI) aboard

the KOrea Multi-Purpose SATellite (KOMPSAT) is designed

to observe the global ocean color in support of biological

oceanography. Since after the successful launch of OSMI on

December 21, 1999, it has been collecting the global ocean

color data in the six visible spectral bands centered at 412,

443, 490, 555, 765, and 865 nm. The level 1A data collection

over worldwide oceans amounts to about 1.2 terabytes as of

August 2001. The OSMI product processing is done through

the OSMI data analysis system (OSMIDAS) that consists of

several processing modules (Kim et al. 1999). One of

OSMIDAS processing modules performs the conversion of the

measured digital number (DN) for level 1A data to the

physical quantity of radiance at the top of the atmosphere

(TOA) and produces level 1B data. Such conversion is called

the absolute radiometric calibration (Dinguirard and Slater,

1999) and requires the pre-launch sensor calibration

information. The pre-launch sensor calibration is particularly

important for the ocean color sensor because the ocean color

information relies upon a 10% to 15% amount of measured

total radiances. The KOMPSAT provider, TRW, delivered the

pre-launch sensor calibration information. The OSMI sensor

also has the capability of solar calibration. It is designed to

monitor the post-launch sensor degradation and thus

complements the pre-launch sensor characterization. We

report the OSMI calibration anomalies that were seen from the

initial radiometric calibration processing and summarize the

ongoing calibration efforts.

25.2 RESEARCH ACTIVITIES

Figure 25.1 shows the OSMI level 1A image over

Arabian Sea acquired on February 27, 2000. The image is not

radiometrically corrected and reveals the bands and stripes

extensively over the oceanic region. Dark and gray bands are

interleaved in each scan of OSMI and narrow stripes

periodically appear in gray bands. Banding is caused by

differences of CCD detector responsivities between positive

(1st to 48th) and negative (49th to 96th) fields. The above

periodic stripes result from low responsivities of along-track

pixels 55, 56, 57, and 58, which cannot be expected from prelaunch calibration measurements. Since the measured digital

numbers over land areas of high reflectivity exceed the

saturation limit, they look white. A few sparse white areas

near Oman and Pakistan are thought to be clouds and sand

dust, respectively. We have applied the pre-launch calibration

data of TRW to this image for radiometric calibration. The

results are shown in Fig. 25.2. The radiometric calibration

processing does not successfully remove the bands and stripes

from the image illustrated in Fig. 25.1. More importantly,

OSMIDAS with these calibrated radiances was not able to

perform the subsequent processing to compute geophysical

variables of level 2 products. We will describe the root cause

of this problem shortly. Another way of performing the DNto-radiance conversion may be to use solar calibration. To

derive the radiometric calibration coefficients (slope and offset

values), we have used the OSMI solar calibration data and the

estimated incoming solar radiance at each pixel of given

spectral bands. See the details of methods in Lee and Kim

(2000). Figure 25.3 shows the results of the radiometric

calibration processing using solar calibration. Bands and

stripes are nearly invisible. However, it is clearly seen that

there are lines between each scan of OSMI. In fact, these lines

exist in Fig. 25.2. We found that such anomalous lines

(hereinafter Venetian-Blind effect) between two neighboring

scans are related to increases of pixel radiance values with

along-track direction. Similar to the first method, we could not

produce the level 2 products with this method for the reason

below. A root cause of failure to produce the level 2 products

is that the measured TOA radiance values at shorter

wavelengths do not overcome the atmospheric Rayleigh

scattering limit. Since the measured TOA radiance consists of

Rayleigh, aerosol, sun glint, and water-leaving radiances, it

should be greater than Rayleigh radiance alone. This is not

true for the OSMI sensor as shown in Fig. 25.4. Regardless of

the radiometric calibration method, the OSMI radiance values

at band 412 nm and band 443 nm are shown to be smaller than

the corresponding Rayleigh radiances and far less than the

collocated SeaWiFS counterparts. It appears to be related to

sensor design characteristics. However, we have not been told

any analysis report regarding sensor anomalies from TRW.
165

Cross-calibration with SeaWiFS Measurements

As mentioned in the previous section, the Rayleigh

scattering limit basically inhibits the generation of the level 2

products. The cross-calibration method using other satellite

measurements may be a solution of this problem. We have

applied a simple method (hereinafter KARI method) to derive

the radiometric calibration coefficients directly from the

collocated match-ups of OSMI DNs and SeaWiFS radiances.

The procedure is as follows: 1) Find the SeaWiFS image that

matches well in time and space with the OSMI image. 2)

Select the purely clear ocean areas from each image. 3)

Resample the SeaWiFS image pixels for collocation, using the

nearest-neighbor method. 4) For the collocated pixels, extract

DNs from the OSMI level 0 data and radiances from the

SeaWiFS level 1B data. 5) Offsetting the solar zenith angle

differences between two data sets, calculate the calibration

coefficients for each along-track pixel of a given spectral

band. As an example, we have obtained the SeaWiFS image

for the same area as shown in Fig. 25.1. The OSMI image was

acquired 15:18 on February 27, 2000, while the SeaWiFS

image was taken 16:42 on the same day. Time here is in

Korean local hour. The solar zenith angles are 37° and 26.5°

for the OSMI image and the SeaWiFS image, respectively.

Following the above procedure, we derive the calibration

coefficients and subsequently produce the level 2 products as

discussed in the next section. Currently, the Korea Aerospace

Research Institute (KARI) collaborates with NASA for the

OSMI data merger as part of the SIMBIOS project. For this

collaboration, the NASA SIMBIOS office is developing a

cross-calibration method (hereinafter NASA method) using

the collocated OSMI and SeaWiFS measurements over the

region of MOBY. The preliminary results will be illustrated in

the following section.

25.3 RESEARCH RESULTS

The KARI method of cross-calibration adequately

overcomes the banding and striping problems as expected

(Fig. 25.5) and resolves the Rayleigh limit issue. The resultant

distribution of chlorophyll concentration, which is one of

OSMI level 2 products, is depicted in Fig. 25.6. It displays the

overall structure of ocean dynamics and reasonably agrees

with the SeaWiFS pattern (not shown). For a few cases

examined so far, including Argentinean coastal region, Great

Australian Bight, and the Korean peninsula, the results are

similar to the case of Arabian Sea. As part of validation

efforts, we compare the TOA radiances of three different

sources at (37.54°N, 129.27°E) off the east coast of Korean

peninsula on May 31, 2000 (Fig. 25.7). Calculation of OSMI

TOA radiances uses the radiometric calibration coefficients

derived from the case of Arabian Sea. We resample the

SeaWiFS data to collocate with OSMI measurements. Inputs

for the model rstar5b run include the in-situ measurements of

aerosol optical thickness and wind speed. The OSMI TOA

radiances lie between the SeaWiFS measurements and model

estimates. Apparently, the OSMI TOA measurements are in

good agreement (5 ~ 17%) with the SeaWiFS measurements at

this location. The model calculation with clear-sky assumption

must have underestimated the radiance values because clouds

reflect more shortwave radiation to the space. It was, in fact,

partly cloudy on the day (May 31, 2000) when in-situ

measurements were obtained. Figure 25.8 shows the level 1B

image that was obtained from the NASA MSl12 program with

the NASA derived cross-calibration coefficients. The banding

and striping problems are fairly well treated. The similar

analysis as shown in Fig. 25.7 will be made for the 2001 insitu measurements.

CONCLUSIONS

We have demonstrated two anomalous features in

performing the OSMI data processing: Venetian-Blind effect

and Rayleigh limit. To resolve these anomalies properly, we

attempted two cross-calibration methods using the collocated

match-ups of OSMI and SeaWiFS measurements. Both KARI

and NASA methods seem to remove the bands and stripes

fairly well and are successful to produce the ocean color (level

2) products. It is however necessary that further efforts should

be made to validate the OSMI products.

ACKNOWLEDGMENTS

We appreciate Prof. B. J. Sohn and Mr. D. H. Kim for

their provision of rstar5b model results. The SeaWiFS data

used in this study are obtained from the NASA Goddard

DAAC.

REFERENCES

Dinguirard, M. and P. N. Slater, 1999: Calibration of spacemultispectral imaging sensors: A review, Remote Sens.

Environ., 68, 194-205.

Kim, Y., Y. S. Kim, H. Lim, D. Lee, C. Kang, 1999:

KOMPSAT data processing system: An overview and

preliminary acceptance test results, J. of the Korean

Society of Remote Sensing, 15(4), 357-365.

Lee, D. H. and Y. Kim, 2000: Radiometric calibration of

OSMI imagery using solar calibration, J. of Astron. Space

Sci. 17(2), 295-308.
166

Figure 25.1: Arabian Sea (OSMI Level 1A, 765 nm)

Figure 25.2: Same image as Fig.25.1 but radiometrically calibrated with TRW calibration data
167

Figure 25. 3: Same image as Fig. 25.1 but radiometrically calibrated with solar calibration data

 Figure 25.4: Comparison of TOA radiances at (20.01°N, 62.51°E) of Arabian Sea

Wavelength (nm)

300 400 500 600 700 800 900

Radiance (mW/cm2

μm sr)

0

2

4

6

8

10

OSMI (solar cal. data)

Rayeigh Radiance

SeaWiFS 

OSMI (TRW cal. data)
168

Figure 25. 5: Same image as Fig. 25.1 but cross-calibrated with KARI method

Figure 25. 6: Distribution of chlorophyll concentration (mg⋅m-3) in Arabian Sea
169

Figure 25.7: TOA radiances at (37.54°N, 129.27°E), May 31, 2000

Figure 25.8: Same image as Fig. 25.1 but cross-calibrated with NASA method

Wavelength (nm)

400 500 600 700 800 900

Radiance (mW cm-2 sr-1 

μ

m

-1)

0

2

4

6

8

10

12

Model (rstar5b)

OSMI 

SeaWiFS 
170

Chapter 26

Evaluation and Improvement of the Atmospheric Correction and

Bio-optical Algorithms for the Black and Barents Seas

Oleg V. Kopelevich, Vladimir I. Burenkov, Svetlana V. Ershova, Marina A. Evdoshenko, Sergey V.

Sheberstov

Ocean Optics Laboratory, P.P.Shirshov Institute of Oceanology, Moscow, Russia

26.1 INTRODUCTION

The overall objective of this research is to evaluate and

improve the atmospheric correction and bio-optical algorithms

for the Black and Barents Seas. The Black and Barents Seas

are the regions currently subjected to increasing human

activity, such as the oil-gas industries and fisheries in both of

the regions; and also agriculture and tourism in the Black Sea.

The operational monitoring of the environment state and

processes in their waters is needed, and the data products

derived from satellite ocean-color sensors can make important

contribution to realization of such kind of monitoring. The

problem is that the both regions qualify as Case 2 waters, with

the Barents Sea falls within high-latitudes, and now there are

no workable processing algorithms for such waters. The great

errors in retrieval of the water-leaving radiances and

chlorophyll concentration with the SeaWiFS operational

algorithms have been detected in these regions as a result of

our previous work done on the SIMBIOS project (Kopelevich

1999a, b, Kopelevich et al. 2001). Specific goals of the current

project as follows:

• Evaluate the operational atmospheric correction and biooptical algorithms for SeaWiFS, GLI and other oceancolor sensors;

• Develop and evaluate the new algorithms making possible

to derive the standard data products with enhanced

accuracy and to retrieve some new parameters such as the

absorption coefficient of yellow substance and the

backscattering coefficient of particulate matter;

• Estimate errors in water-leaving radiances, seawater

optical characteristics, and chlorophyll concentration

derived with different algorithms;

• Draw the conclusions concerning performance of the

atmospheric correction and bio-optical algorithms in

turbid (Case 2) waters and at high solar zenith angles.

26.2 RESEARCH ACTIVITIES

Field studies

The field studies were carried out in the White and Black

Seas in August 2001. In Figure 26.1 the station locations of

the cruise # 49 of RV Professor Shtockman in the White Sea

are shown. The studies on ocean optics and biogeochemistry

included measurements of spectral water-leaving radiance just

beneath sea surface and down-welling irradiance just above

sea surface by a floating spectroradiometer (Artemiev et al.

2000), vertical profiles of the beam attenuation coefficient by

a submersible transmissometer, Secchi depth, underwater

irradiance in the UV and PAR spectral ranges by a UV-PAR

radiometer (Burenkov et al 2001), sampling and filtration of

seawater samples from different depths, setting up the

sediment traps, determination of size spectra of suspended

particles by Coulter Counter, sampling aerosol particles by

nets and pumps. Measurements of the spectral remote sensing

reflectance were carried out at 8 drift stations, one of them (4

August 2001) was performed simultaneously with SeaWiFS

pass under clear sky conditions.

The studies in the Black Sea included measurements of

spectral water-leaving radiance above the sea surface, sky

radiance and surface irradiance by a deck spectroradiometer,

vertical profiles of the beam attenuation coefficient by a

submersible transmissometer, sampling from different depth

for determination of chlorophyll concentration. Ship

measurements by the deck spectroradiometer were carried out

onboard RV Akwanavt during its steaming and at drift stations

off the Russian coast from Gelendjick to Yuzhnaya Ozereevka

(44o

31’-44o40’N, 37o36’-38o03’E) on 16, 18, 19 and 20

August 2001. On 23-29 August the instrument was mounted

on the roof of the pier laboratory in the Blue Bay (near

Gelendjick) and measured the surface irradiance and zenith
171

sky radiance with the aim of validation of the algorithm for

retrieval of surface irradiance from SeaWiFS data under clear

sky and cloud conditions (Ershova et al. 2001). A list of

measurements by the deck spectroradiometer is given in Table

26.1. Since the beginning of October 2001 the ocean color

collaborative US-Russia studies have been conducted onboard

RV Akademik Ioffe as its transits the Atlantic Ocean and then

during its tourist cruises in the Antarctic Regions. The ship left

Kaliningrad (Russia) for Ushuoya (Argentine), the scientific

equipment prepared by Robert Frouin (Scripps Institution of

Oceanography) and Michael Reynolds (Brookhaven National

Laboratory) was loaded in Kiel and Bremerhaven. On 17

November 2001 the ship came to Ushuoya; since 18

November it has made the tourist cruises from Ushuoya to

Antarctica and back (duration of each cruise is 8-18days).

The various atmospheric and ocean characteristics have

been studied during the cruises: the aerosol optical thickness

and diffuse marine reflectance by the SIMBAD radiometer;

the remote sensing reflectance by the deck spectroradiometer

of the Shirshov Institute of Oceanology; the direct, diffuse and

global irradiance with the Portable Radiation Package of the

Brookhaven National Laboratory; chlorophyll-a concentration

by a spectrophotometric method (Shirshov Institute of

Oceanology); sampling, handling and storage in liquid

nitrogen samples for HPLC pigment analyses (the equipment

provided by Robert Frouin); seawater spectral absorption

coefficient by the laboratory spectrophotometer of the

Shirshov Institute of Oceanology; sampling, sample handling

and determination of spectral absorption coefficients of

particles, dissolved material and phytoplankton for surface

water samples (the equipment provided by Robert Frouin).

The ship will leave Ushuoya for the return trip in the

middle of March 2002. The studies performed are expected to

provide data on spatial changeability of atmospheric and

oceanic characteristics in the Atlantic Ocean between 55o

N

and 55o

S in two different seasons as well as their mesoscale

variability for poorly studied area south of 55oS from

November to March, which will be compared with concurrent

satellite data.

Algorithm development

The simplified algorithm to derive the particle

backscattering coefficient bbp has been developed. The bbp

values can be retrieved from ocean color data by the

semianalytic bio-optical algorithm but the latter can not work

in the cases of bad atmospheric correction, for example, of

turbid waters or observations under conditions of large solar or

viewing zenith angles (Burenkov et al. 2001). In such cases

the errors of atmospheric correction are most great at 412 and

443 nm and much less at 510 and 555 nm (Kopelevich et al.

2000). The simplified algorithm uses only two SeaWiFS

spectral bands 510 and 555 nm and allows to determine the

particle backscattering coefficient bbp(555). The bb(555) value

is derived through the values of the parameter

X(555)=bb(555)/[a(555)+bb(555)] and the diffuse attenuation

coefficient Kd(555); these quantities are determined from the

normalized water-leaving radiances Lwn(510) and Lwn(555); the

former is directly from Lwn(555), the latter through the ratio of

these radiances.

The regression equation between the bbp(555) values and

the suspended matter concentration SPM was derived from in

situ measured data in the Barents Sea: SPM = 73.5 bbp(555) +

0.016, where SPM in g⋅m-3, bbp(555) in m-1.

Correlation between these quantities is reasonably good; the

coefficient of determination R2

 is equal to 0.83, the relative

root-mean-square error is about 30%. The developed

algorithms were used to derive the mean monthly distributions

of the particle backscattering coefficient and the suspended

matter concentrations in the Barents Sea from SeaWiFS data.

26.3 RESEARCH RESULTS

Results of field studies

As a new result the measured data in the White Sea

should be mentioned. This sea is located south to the Barents

Sea and connected with it by the strait named Throat of the

White Sea. The strong tidal streams through the strait (up to 2

m/s) promote the water exchange in upper layer between the

basins. Several rivers (Northern Dvina, Mezen, Onega and

others) flow into the White Sea, and it is interesting to make

comparison between two neighboured basins. Three examples

of the radiance reflectance spectrum measured in the White

Sea are given in Figure 26.2. The similarity of the spectra to

one another can be noticed, even though they were measured

in different parts of the White Sea (see Fig.26.1). All of them

have the maximum near 560-570 nm which is indicative of

high concentration of colored organic matter (“yellow

substance”). The above maximum is higher at St.4681 with

larger value of the beam attenuation coefficient and probably

with greater concentration of suspended matter. Also all

spectra have the other maximum near 680 nm which is due to

chlorophyll fluorescence and indicative of its high

concentration. The data will be analyzed in detail in 2002.

Use of the simplified algorithm

Figure 26.3 shows the mean monthly distributions of the

particle backscattering coefficient bbp(555), m-1 in the Black

Sea in June and September derived from SeaWiFS data by the

simplified algorithm. The highest values of bbp (more than

0.02 m-1) are caused predominantly by river runoff; the

pronounced seasonal changeability can be noticed. The main

source of suspended matter in the Black Sea is Danube; its
172

influence is seen the year round. The area of enhanced values

of bbp in the eastern part of the Black Sea is caused by runoff

of many small rivers which dry up in summer.

Figure 26.4 shows the mean monthly distributions of the

suspended matter concentration Cs in the Barents Sea in May

and July 2000. The most turbid waters (Cs >1-2 mg/l) are

observed in coastal zone and connected with river runoff,

coastal erosion, resuspension of bottom sediments by tidal

currents. The noticeable difference between May and July is

seen. The area of turbid waters in the southern part of the

Barents Sea is bigger in May when the river runoff is the

greatest. The concentration of suspended matter in the central

part of the Barents Sea is also higher in May, probably, due to

spring phytoplankton bloom. In July the suspended matter

concentration in the central part of the Barents Sea is lowest.

The spatial distributions of suspended matter in the Barents

Sea constructed from SeaWiFS data is in a good agreement

with field data but satellite data provide us with much more

information.

26.4 FUTURE WORK

Development of the new algorithms to improve an

accuracy of the standard data products and retrieve some new

parameters in such specific basins as the Black, Barents and

White Seas is only possible on the base of in situ measured

data. At the moment our future plans for the field studies are

to be determined. The analysis of data obtained in 2001 and in

January-April 2002 during the ongoing Ioffe cruise will be

performed in 2002. Comparison between different regional

algorithms will allow to understand which level of generality

can be acceptable for Case 2 waters.

REFERENCES

Artemiev, V.A., V.I.Burenkov, M.I.Vortman, A.V.Grigoriev,

O.V.Kopelevich, and A.N.Khrapko, 2000: Sea-Truth

Measurements of Ocean Color: A New Floating

Spectroradiometer and Its Metrology. Oceanology,

Vol.40, 139-145. Translated from Okeanologiya, Vol.40,

148-155.

Burenkov, V.I., V.I.Vedernikov, S.V.Ershova,

O.V.Kopelevich, S.V.Sheberstov, 2001: Use of Satellite

Ocean Color Data for Assessment of Bio-Optical

Characteristics in the Barents Sea. Oceanology, Vol.41.

Translated from Okeanologiya, Vol.41, 485-492.

Ershova, S.V., O.V.Kopelevich, S.V.Sheberstov,

V.I.Burenkov, and A.N.Khrapko, 2001: A Method for

Estimating the Penetration of Solar Visual and Ultraviolet

Radiation into the waters of the Arctic Seas Using

Satellite Data: The Case of a Cloudless Atmosphere.

Oceanology, Vol.41, 317-325.

Kopelevich, O.V., 1999a: Validation of the Water-Leaving

Radiance Data Product .In: C. McClain, G. Fargion:

SIMBIOS Project 1998 Annual Report. NASA Tech.

Memo. 208645,NASA Goddard Space Flight Center,

Greenbelt, Maryland, 88-93.

Kopelevich, O.V., 1999b: Validation of the Water-Leaving

Radiance Data Product .In: C.R. McClain and G.S.

Fargion: SIMBIOS Project 1999 Annual Report. NASA

Tech. Memo. 209486, NASA Goddard Space Flight

Center, Greenbelt, Maryland, 121-126.

Kopelevich, O.V., V.I.Burenkov, S.V.Ershova,

S.V.Sheberstov, M.A.Evdoshenko, G.K.Karabashev,

C.A.Pavlov, 2001: Validation of the Water-Leaving

Radiance Data Product .In: G. Fargion and c. McClain:

SIMBIOS Project 2000 Annual Report. NASA Tech.

Memo. 209976, NASA Goddard Space Flight Center,

Greenbelt, Maryland, 157-162.
173



















     











































































  

  



   





 

 











	

	













 	















 Figure 26.1: The station locations of the cruise # 49 of RV Professor Stockman in the White Sea.

 The station locations in the south part of the Dvina Bay are shown on the setting-in.

Figure 26.2: Radiance reflectance spectra measured in the White Sea at different stations. The values of the

beam attenuation coefficient were 0.97 m-1 at St.4681, 0.76 m-1 at St.4716, and 0.73 m-1 at St.4721.

400 500 600 700

w avelength, nm

0.00

0.01

0.02

0.03

spectral radiance reflectance

4681

4721

4716
174

 A B

Figure 26.3: The mean monthly distributions of the particle backscattering coefficient bbp(555), m-1 in the Black Sea in June

(A) and September (B) derived from SeaWiFS data in 1999.

 A B

 Figure 26.4 : The mean monthly distributions of the suspended matter concentration (mg/l) in the Barents Sea

 in May (A) and July (B) derived from SeaWiFS data in 2000.
175

Table 26.1: Measurements by the deck spectroradiometer in the Black Sea in August 2001

Date Local time of SeaWiFS

measurements* Time of the pass Elevation, deg

Location Sky condition

16 Aug 01 13:34 – 18:59 13:40 51.5 Near Gelendjick Clear

18 Aug 01 11:06 – 18:50 13:26 38.4 Blue Bay- Novorossisk Clear

19 Aug 01 09:30 – 19:43 14:09 82.5 Near Yuzhnaya Ozereevka Clouds

20 Aug 01 11:51 – 16:55 14:51 33.0 Near Blue Bay Clouds, rain

23 Aug 01 07:49 – 19:43 13:43 55.5 Blue Bay Clouds

24 Aug 01 07:27 – 19:41 14:25 57.6 Blue Bay Clouds

25 Aug 01 07:33 – 19:39 13:30 41.2 Blue Bay Clear

26 Aug 01 07:28 – 19:37 14:13 77.1 Blue Bay Clouds

27 Aug 01 07:22 – 19:34 13:16 31.0 Blue Bay Clouds

28 Aug 01 07:24 – 19:34 14:00 80.3 Blue Bay Clear

29 Aug 01 07:39 – 15:43 14:41 40.4 Blue Bay clouds *

 the time difference between the local time and GMT is + 4 hours
176

Chapter 27

VALIDATION OF SEAWIFS OCEAN AND ATMOSPHERIC

PRODUCTS IN THE MEDITERRANEAN SEA

R. Santoleri, G.L. Liberti, G.P. Gobbi and F.Barnaba

Istituto di Scienze dell’Atmosfera e del Clima – ISAC, Italy

M. Ribera d’Alcalà, F. D’Ortenzio, M. Ragni, G.L. Volpe and E. Bohm

Stazione Zoologica A. Dorn di Napoli – SZN, Italy

 S. Marullo 

Ente Nazionale per le Nuove tecnologie l’ Energia e l’Ambiente – ENEA, Italy

27.1 INTRODUCTION

The objective of this report is to describe the activities

linked to the SIMBIOS project carried out during the year

2001 by the joint ISAC and SZN group. The goal of this

research was: 1) to assess the accuracy of satellite ocean color

products in the Mediterranean Sea. 2) to develop new regional

ocean color algorithms for the Mediterranean Sea that include

improved atmospheric correction (Mediterranean aerosols)

and retrieval of bio-optical parameters. 3) to design and

execute oceanographic campaigns to investigate particularly

the relationship between mesoscale features and biological

response over selected areas and validate Mediterranean

algorithms.

The Mediterranean Sea is a mid-latitude, predominantly

oligotrophic and ultra-oligotrophic basin. However, higher

biomass may locally and seasonally occur in regions affected

by river runoff or by deep convection events (Antoine et al.,

1996). This basin is a good oceanographic test area both for its

complex ocean dynamics, which mimics several basic

processes of ocean functioning and for its intensive

anthropogenic pressure. From the atmospheric point of view,

the Mediterranean area is strongly affected by anthropogenic

emissions from the highly industrialized northern border and

desert dust from the south. Aerosols transported to the

Mediterranean Sea may be considered to consist of

anthropogenic-rich "background" materials supplied

continuously from Europe, upon which sporadic pulses of

Saharan crust-rich dust are superimposed. Schematically, the

latter represent more than 90% of the mass of particulate

atmospheric deposition, though they occur 10% of the time in

the Mediterranean atmosphere, whereas the inverse figures

apply to anthropogenic aerosols. The very peculiar aerosol

composition makes more difficult the use of routine remote

sensing procedures for atmospheric correction (Moulin et al.,

1997). In parallel, previous study on CZCS data in this basin

(Morel and André, 1991; Antoine et al., 1996) showed that the

global ocean-color bio-optical algorithm often results in a poor

estimate of the chlorophyll-a.

All the considerations above imply the need of a special

validation effort for the Mediterranean Sea and eventually the

development of new regional ocean color algorithms.

Development of specific regional algorithms requires, as a

starting point the evaluation of current SeaWiFS standard

products to identified and understand areas possible sources of

errors and design acivities for their handling. Within this

frame, the following strategy was adopted in this first year

activity. Firstly, independent teams with specific experience

analysed separately the SeaWiFs current atmospheric

correction scheme and the bio-optical algorithms. At the same

time, a joint effort in collecting data to enlarge the available

data set for studies was carried out by both teams with a

dedicated effort in data acquisition both from land based

instrumentation as well as from specific oceanographic

campaigns. In the following a brief description of activities is

reported.

27.2 RESEARCH ACTIVITIES

In situ data acquisition

During the 2001 oceanographic in situ data acquisition

consisted in: a major oceanographic cruise, Norbal 2001,

carried out in December in the Western Mediterranean Sea,
177

three additional cruises in which only SIMBAD measurements

were performed and weekly measurements in the Gulf of

Naples.

During the NORBAL 2001 cruise, 62 hydrological

profiles (CTD casts) have been carried out along transects

covering different region of the West Mediterranean: North

Tyrrhenian, the Ligurian Sea and the Gulf of Lion. In a

selected number of stations, samples for Winkler

determination of dissolved organic and inorganic nutrients

were collected to characterize water mass properties and

associated fluxes. Around mid-day bio-optical stations were

performed. In this station in water bio-optical profiles were

acquired by means of a Satlantic SPMR radiometer, vertical

profiles of PAR and variable fluorescence were perfomed. In

the bio-optical stations phytoplackton lipophilic pigment

distribution (HPLC and spectrofluorometer analysis) and

ancillary biological data were also acquired. Moreover, during

the daytime several measurements of upwelling radiances at

ocean colour wave length and solar irradiance and sky

irradiance were measured using SIMBAD instrument. Finally,

meteorological parameters, sea surface temperature, salinity

and fluorescence were measured continuously along the ship

track. Vertical profiles of horizontal velocities were also

acquired continuously (surface-to 300 m) by mean s of two on

board ADCP currentmeters. In figure 1 the position of the

CTD station were reported.

Thanks to an agreement with other Italian research

Institution we take the opportunity to carried out SIMBAD

measurements during three addition cruises carried out in

March, September and October 2001. Actually a total of 45

days of measurements of water leaving radiances, atmospheric

aerosol load and type were acquired in the Mediterranean Sea

during 2001. Data are currently under processing at LOD

(Laboratoire d’Optique Atmospherique) and will be available

at the SIMBAD web site.

In addition a weekly station in which CTD profile, water

samples and bio-optical measurements were taken in the Gulf

of Naples. Using the fact that the Gulf of Naples station is

reached with a relatively fast motorboat an experiment was

designed based on frequent atmospheric measurements with

SIMBAD to evaluate the variability of the AOT from land to

coast. Results from such an experiment would be used to

have a first estimate of uncertainties derived from the use of

land based coastal site for the validation of off-shore aerosol

optical properties. Unfortunately, the first attempt of such

experiment failed because of problems with the SIMBAD

instrument. The experiment will be repeated, as often as

possible, during the next year depending on SIMBAD

availability.

Land Observations

Giving the relatively high frequency of occurrence

anomalous stratification in the vertical aerosol distribution, in

the Mediterranean basin, as for example in the case of desert

dust transport events and the expected impact on remotely

sensed ocean colour, a particular effort have been concentrated

in the description of the vertical distribution of aerosol

extinction. A technique to derive aerosol extinction profiles

from single-wavelength (532 nm) lidar measurements have

been improved by adding continental aerosol types to the

aerosol types already accounted for (stratospheric volcanic

(Gobbi, 1995); maritime and desert dust (Barnaba and Gobbi,

2001, Gobbi et al., 2000).

 At the same time we are collecting a yearly record (Feb.

2001-Feb.2002) of coincidental lidar and sunphotometer

(AERONET site Rome Tor Vergata) observations to validate

such inversion schemes and to study the impact of aerosols (in

particular desert dust) on solar radiation. The outcome of these

studies will have an impact on all lidar and photometric

activities relevant to SIMBIOS.

27.3 RESEARCH RESULTS

Evaluation of the performances of the atmospheric correction

scheme in the case of Mediterranean basin

Preliminary results of a validation exercise aimed to

evaluate the SeaWiFS aerosol products goodness over the

Mediterranean Basin are presented in this section, (see also

Liberti et al., 2000). The validation data set is based on

observations from AERONET (Holben et al., 1998) groundbased CIMEL sun-photometers measurements made in sites

around the Mediterranean within 100 km from the coast. The

following sites were selected: Avignon, El Arenosillo,

Erdemli, Lampedusa, Nes Ziona, Oristano, Venezia. Data

from the EU campaign ADMIRA in Nea Michaniona (Webb

et al.2002) were also used.

The data analyzed derive from single SeaWiFS orbit data

collected at the receiving station of Roma Tor Vergata

(41°50’N 12°39’E), during the whole year 2000, and

successively processed to level 2.0 geophysical products using

SEADAS processing Version 4.0b (Gordon and Wang 1994,

Wang 2000) with the following options that are the ones

adopted for the standard NASA products: Multi-scattering

atmospheric corrections; Aerosol model selection based on the

analysis of the 765 and 865 nm radiances; Siegel (Siegel et al.

2000) NIR iterations; land and straylight flags were activated.

Level 2 processed data were successively remapped on a

Cylindrical Equirectangular Projection with a reduced ground
178

Figure 27.1: Area of Norbal 2001 cruise measurements and position of CTD stations.

resolution of about 4 km obtained with a nearest neighbour

sampling. The resulting data set consists into about 670 single

orbit images with very few data gaps, mostly occurring

between September and October.

CIMEL-AERONET instruments and the SeaWiFS

radiometer have only three common wavelengths: 440, 670

and 875 nm. Therefore, as a preliminary analysis, aerosol

optical thickness at 670 nm ( λ ) and the Ångstrom coefficient

between 670 and 875 nm were compared. The evaluation of

the SeaWiFS products have been performed with several

parallel activities:

• the first type of evaluation is based on a subjective

analysis of each single orbit product map as well as

monthly and seasonal composites ones. From such an

analysis it is expected to identify evident algorithm

behaviours such as the occurrence of clearly not-physical

features/values, consequence, for example, of calibration,

observation geometry and cloud screening.

• a second type statistical or pseudo-climatological

comparison of SeaWiFS aerosol derived products against

ground truth derived ones. The rationale for this

comparison is that climatologies of geophysical

parameters for the same areas and periods, should

compare also if areas and period do not match exactly but

refer to the same climatological regime.

• Finally, a match-up comparison was performed. The

matching criteria have been determined by analysing

spatial and temporal variability characteristics of the

aerosol products. Fig. 27.2 shows the comparison of

match-up results obtained using ground truth data in a +/-

0.5 hr time window from the satellite overpass and in a

space window of 40 km around the ground truth data. A

relatively good agreement for the aerosol optical

thickness is found. On the contrary, the comparison of α

shows a very poor agreement between AERONET in situ

and SeaWiFS estimations. On the other hand, part of the

relatively large in situ values of α come from the
179

Oristano station, which we suspect to have a calibration

problem which produces larger values of α at local

noon.

The main conclusions from such preliminary comparison are:

• relatively good agreement for the aerosol optical

thickness at NIR wavelengths.

• underestimation of the Angstrom coefficients because of

lack of models covering part of the commonly observed

values in the Mediterranean Sea.

On the basis of the analysis of ground truth data and

recent literature on characterization of aerosols in the

Mediterranean Area, it seems that the SeaWiFS candidate

aerosol models are some how too maritime. In fact,

Mediterranean Sea is relatively a close basin, with low wind

intensity regimes and under the influence of strong

continental-like, both antropogenic and natural, sources of

aerosols. An interesting feature is the selection of tropospheric

aerosol for relatively polluted area. Giving the fact that the

aerosol model selection is based on the spectral dependence of

a signal in a relatively narrow region of the NIR spectrum

(760 and 870 nm) there may be more than one aerosol having

in such region the same spectral dependence. This is roughly

true for the two main components of the Tropospheric (i.e. the

water soluble) and the Urban (i.e. the soot). Similarly the Sea

Salt shows in the NIR a spectral dependence relatively similar

to that of the Desert Dust or of the Clouds.

On the other hand, the relatively good agreement in

retrieving NIR aerosol optical thickness may be explained by

the fact that the two wavelengths used for the aerosol model

selection (i.e. 760 and 870 nm) are relatively close so that the

optical characteristics other than the aerosol optical thickness

(representative somehow of the amount of aerosol) i.e. the

single scattering albedo and the phase function (specific of the

aerosol model) are relatively similar among the ones of the

candidate models. In such a way the main source of variability

in the measured radiance is the optical thickness.

In terms of chlorophyll retrieval a wrong estimate of the

Angstrom coefficient may have a strong impact. In fact, the

Angstrom coefficient is responsible for the colour of the

atmosphere and wrong evaluation of the spectral dependence

of the aerosol component may introduce in the water leaving

reflectances undesired spectral behaviour.

Validation of SeaWiFS aerosol products has been already

performed (Wang et al. 2000). Although, there are few

differences in the match up criteria that are aimed to select

relatively clear (τ < 0.2) and stable days, the results are

similar: i.e. a good agreement between AOT’s at the NIR

wavelengths while an underestimation going to shorter

wavelengths. This is consistent with our results. Although, the

sites are not in the Mediterranean Sea, they still are on coasts

of the Northern Hemisphere. It is interesting to notice that

similar results were obtained in the validation POLDER

aerosol products over ocean (Goloub et al. 1999).

Evaluation of empirical SeaWiFS bio-optical algorithms for

chlorophyll-a retrieval in the Mediterranean

The major aim of this section is the validation of some

representative empirical algorithms to determine their

performance in retrieving chlorophyll-a concentration in the

Mediterranean Sea from SeaWiFS data (see also D’Ortenzio et

al. 2001). We selected three algorithms: OC2v4 and OC4v4 as

NASA’s operational algorithms, and GIT (Gitelson et al.,

1996), as an example of a regional Mediterranean algorithm.

In view of the SeaWiFS ocean color algorithms’

validation in the Mediterranean Sea, a specific data set of biooptical and pigment concentration measurements has been

built by SZN group in the period 1998-2000 in the framework

of the SYMPLEX (SYnoptic Mesoscale and PLankton

EXperiment) Project of the Italian Space Agency (ASI).

During SYMPLEX project four oceanographic cruises were

performed on board the R/V Urania of the Italian Research

Council. During each cruise, water samples were taken from

Niskin bottles mounted on a General Oceanics Rosette

equipped with a SBE 911 CTD profiler and a SeaTech

Flurometer. Sub-samples to measure chlorophyll-a (C) and

phaeophytin-a (P) were filtered on board on GF/F filters (low

vacuum) and immediately deep-frozen. Pigment

concentrations were subsequently determined at the SZN on

90% acetone extracts within few weeks of the sampling using

a SPEX Fluorolog spectrofluorometer with an estimated

coefficient of variation for chlorophyll-a concentration of ca.

10% (Neveaux and Panouse, 1987). For each cruise, we

merged and linearly fitted spectrofluorometrically derived

chlorophyll-a concentrations with data acquired by the

SeaTech in situ fluorometer. Therefore, the data set consists of

582 chlorophyll-a profiles derived from the calibrated

fluorescence profiles. We also conducted optical

measurements at selected sites during each cruise, totally 45

stations. In water downwelling irradiance (Ed) and upwelling

radiance (Lu) profiles were taken in 32 stations using a

Satlantic SPMR (SeaWiFS Profiling Multichannel

Radiometer). The data were acquired following the standard

SeaWiFS protocols (Mueller & Austin, 1995). The instrument

had been calibrated at Satlantic Inc. just prior to each cruise.

The above water measurements were made in the remaining

13 stations (when SPMR were not available) using the

SIMBAD radiometer. SIMBAD data were then processed at

LOA of the University of Lille (Fougnie et al., 1998). The

final data set covers a relatively wide range of conditions in

Case 1 waters of the Mediterranean Sea, spanning from

oligotrophic (Sicily Channel and Ionian Sea) to moderately
180

Figure 27.2: SeaWiFS products (X-axis) vs Ground Truth ones (Y-axis): τ (left) and α (right). Measurements have been

matched using the +/-0.5 hr time window for the ground truth data and the closest not-empty distance class for SeaWiFS

products

eutrophic regimes (North-Western Mediterranean).

Chlorophyll-a concentrations vary between 0.03 and 2.75

mg/m3, though low values (< 0.1 mg/m3) are definitely more

numerous (≈ 70%). Most of them were sampled during the

stratified season at sites exhibiting one, or seldom two, Deep

Chlorophyll Maxima (DCM) located in the 55-85 m depth

interval. To test the performance of different algorithms in

retrieving chlorophyll-a concentrations independently from

atmospheric correction errors, we used only situ optical

measurements (Rrs) and concurrent in situ chlorophyll-a data.

Bio-optical estimates of chlorophyll-a (Cmod) were obtained

by introducing the in situ Rrs measurements in the three

selected algorithms. For algorithms requiring data at

wavelengths different from those available from in situ optical

measurements (i.e. GIT and OC4v4 when applied to

SIMBAD data), Rrs estimates have been generated using the

interpolation procedure suggested by O’Reilly et al. (2000).

The results of this validation are shown in Figure 27.2.The

analysis revealed a systematic overestimation of chlorophyll-a

concentration by NASA global algorithms (OC2v4 and

OC4v4). The error appears to be correlated with chlorophyll-a

concentration, by exhibiting marked differences at low values

(C < 0.15 mg/m3). In particular at low concentration, the bias

observed for OC2v4 is about twice that observed for OC4v4.

The same analysis made using the Gitelson et al. (1996) CZCS

regional algorithm revealed that this model underestimate the

pigments concentration but it does not exhibit a correlation

between the error and the measures. The algorithm validation

suggests that a regional algorithm is needed for the

Mediterranean Sea. Even if the bio-optical data set presented

above consists of a limited number of data (45 stations), they

represent most of the Mediterranean conditions ranging from

oligotrophic to moderately eutrophic regimes. It is then

possible to use this dataset to develop a preliminary version of

a Mediterranean ocean color algorithm. Two new

Mediterranean algorithms are then proposed by fitting our

Mediterranean bio-optical data set with a linear (L-DORMA)

and an OC2-like functional forms (NL-DORMA):

10 0.035 (0.217 2.728 0.704 0.297 ) 2 3

= − − R+ R + R C

( 2.51 ) 1.49*10 R C − =

where R=log10(Rrs490/Rrs555)

 Finally, we performed a validation of the algorithms by

comparing satellite estimates with concurrent in situ

chlorophyll-a observations. The validation procedure was

applied to the selected algorithm as well as to the new

Mediterranean algorithm. Moreover, we considered also the

Neural Network Algorithm (NNA), to complete the list of the

algorithms provided by NASA‘s standard processing system

(Gross et al., 2000). We opportunely modified the SeaDAS

code to allow the application of the tested bio-optical

algorithms, which are not present in the used SeaDAS

software version. Chlorophyll maps have been flagged by

applying all of the 24 masks provided by SeaDAS (Baith et

al., 2001). This implies that Saharan dust events have been

implicitly excluded by our analysis. The scatter plots of

satellite chlorophyll estimates versus in situ measurements are

shown in figure 4. In this case, the OC2v4 algorithm exhibits

better estimates than OC4v4, which is probably more affected

by atmospheric correction problems. When applied to satellite

data, the GIT algorithm performs better than the NASA global
181

algorithms, even though the estimates are very poor in the

high chlorophyll-a range. The new algorithms perform well

when applied either to the bio-optical measurements or to

satellite data. The main conclusions of the work are: the actual

SeaWiFS algorithms selected are not able to give chlorophylla estimates for the Mediterranean Sea satisfying NASA

requirements (i.e. 35% error in chlorophyll-a concentration).

The analysis of bio-optical measurements revealed a

systematic overestimation of chlorophyll-a concentration by

NASA global algorithms. The error appears to be correlated

with chlorophyll-a concentration, by exhibiting marked

differences at low values (C < 0.15 mg/m3). In particular at

low concentration, the bias observed for OC2v4 is about twice

that observed for OC4v4. The different behaviour of the same

algorithm when applied to bio-optical measurements or to

remotely sensed data demonstrates that the atmospheric

correction is still the main source of error in ocean color data.

Due to the relatively small number of available in situ data,

the algorithms that we generated have to be considered very

preliminary, but clearly reveal the necessity of the

development of a Mediterranean regional algorithm. A

discussion on the reasons of the global algorithms misfit,

providing possible explanations and some preliminary result

can be found in D’Ortenzio et al., (2001).

Figure 27.3: Algorithm validation using in situ bio-optical measurements and concurrent in situ chlorophyll-a data CM : (a)

scatter plot of OC2v4 model values versus CM; (b) scatter plot OC4v4 model values versus CM; (c) scatter plot of GIT model

values versus CM. The 1:1 (center thick line) 1:2 (bottom thin line) and the 2:1 (top thin line) lines are also plotted.
182

 Figure 27.4: SeaWiFS chlorophyll-a estimates (Csat) validation against concurrent in situ chlorophyll-a data Csitu: (a) scatter

plot of SeaWiFS estimate using OC2v4 model values versus Csitu; (b) scatter plot of SeaWiFS estimate using OC4v4 model

values versus Csitu; (c) scatter plot of SeaWiFS estimate using Neural Network model values versus Csitu; (d) scatter plot of

SeaWiFS estimate using GIT model values versus Csitu; (e) scatter plot of SeaWiFS estimate using NL-DORMA model values

versus Csitu. (f) scatter plot of SeaWiFS estimate using L-DORMA model values versus Csitu. The 1:1 (center thick line) 1:2

(bottom thin line) and the 2:1 (top thin line) lines are also plotted.
183

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